These examples show how to fit and predict with different combinations of model, mode, and engine. As a reminder, in parsnip,
the model type differentiates basic modeling approaches, such as random forests, logistic regression, linear support vector machines, etc.,
the mode denotes in what kind of modeling context it will be used (most commonly, classification or regression), and
the computational engine indicates how the model is fit, such as with a specific R package implementation or even methods outside of R like Keras or Stan.
The following examples use consistent data sets throughout. For regression, we use the Chicago ridership data. For classification, we use an artificial data set for a binary example and the Palmer penguins data for a multiclass example.
bart() models
With the "dbarts" engine
Regression Example (dbarts)
We’ll model the ridership on the Chicago elevated trains as a function of the 14 day lagged ridership at two stations. The two predictors are in the same units (rides per day/1000) and do not need to be normalized. All but the last week of data are used for training. The last week will be predicted after the model is fit.
## ── Attaching packages ─────────────────────────── tidymodels 1.1.0.9000 ── ## ✔ broom 1.0.5 ✔ rsample 1.1.1
## ✔ dials 1.2.0 ✔ tibble 3.2.1
## ✔ dplyr 1.1.2 ✔ tidyr 1.3.0
## ✔ infer 1.0.4 ✔ tune 1.1.1
## ✔ modeldata 1.2.0 ✔ workflows 1.1.3
## ✔ parsnip 1.1.1 ✔ workflowsets 1.0.1
## ✔ purrr 1.0.2 ✔ yardstick 1.2.0
## ✔ recipes 1.0.7 ## ── Conflicts ─────────────────────────────────── tidymodels_conflicts() ──
## ✖ purrr::discard() masks scales::discard()
## ✖ dplyr::filter() masks stats::filter()
## ✖ dplyr::lag() masks stats::lag()
## ✖ recipes::step() masks stats::step()
## • Use tidymodels_prefer() to resolve common conflicts.
tidymodels_prefer()
data(Chicago)
n <- nrow(Chicago)
Chicago <- Chicago %>% select(ridership, Clark_Lake, Quincy_Wells)
Chicago_train <- Chicago[1:(n - 7), ]
Chicago_test <- Chicago[(n - 6):n, ]We can define the model with specific parameters:
bt_reg_spec <-
bart(trees = 15) %>%
# This model can be used for classification or regression, so set mode
set_mode("regression") %>%
set_engine("dbarts")
bt_reg_spec ## BART Model Specification (regression)
##
## Main Arguments:
## trees = 15
##
## Computational engine: dbartsNow we create the model fit object:
## parsnip model object
##
##
## Call:
## `NULL`()The holdout data can be predicted:
predict(bt_reg_fit, Chicago_test) ## # A tibble: 7 × 1
## .pred
## <dbl>
## 1 20.1
## 2 20.3
## 3 21.3
## 4 20.2
## 5 19.4
## 6 7.51
## 7 6.44
Classification Example (dbarts)
The example data has two predictors and an outcome with two classes. Both predictors are in the same units.
library(tidymodels)
tidymodels_prefer()
data(two_class_dat)
data_train <- two_class_dat[-(1:10), ]
data_test <- two_class_dat[ 1:10 , ]We can define the model with specific parameters:
bt_cls_spec <-
bart(trees = 15) %>%
# This model can be used for classification or regression, so set mode
set_mode("classification") %>%
set_engine("dbarts")
bt_cls_spec ##
## Call:
## NULLNow we create the model fit object:
## parsnip model object
##
##
## Call:
## `NULL`()The holdout data can be predicted for both hard class predictions and probabilities. We’ll bind these together into one tibble:
## # A tibble: 10 × 3
## .pred_class .pred_Class1 .pred_Class2
## <fct> <dbl> <dbl>
## 1 Class2 0.352 0.648
## 2 Class1 0.823 0.177
## 3 Class1 0.497 0.503
## 4 Class2 0.509 0.491
## 5 Class2 0.434 0.566
## 6 Class2 0.185 0.815
## 7 Class1 0.663 0.337
## 8 Class2 0.392 0.608
## 9 Class1 0.967 0.033
## 10 Class2 0.095 0.905
boost_tree() models
With the "xgboost" engine
Regression Example (xgboost)
We’ll model the ridership on the Chicago elevated trains as a function of the 14 day lagged ridership at two stations. The two predictors are in the same units (rides per day/1000) and do not need to be normalized. All but the last week of data are used for training. The last week will be predicted after the model is fit.
library(tidymodels)
tidymodels_prefer()
data(Chicago)
n <- nrow(Chicago)
Chicago <- Chicago %>% select(ridership, Clark_Lake, Quincy_Wells)
Chicago_train <- Chicago[1:(n - 7), ]
Chicago_test <- Chicago[(n - 6):n, ]We can define the model with specific parameters:
bt_reg_spec <-
boost_tree(trees = 15) %>%
# This model can be used for classification or regression, so set mode
set_mode("regression") %>%
set_engine("xgboost")
bt_reg_spec ## Boosted Tree Model Specification (regression)
##
## Main Arguments:
## trees = 15
##
## Computational engine: xgboostNow we create the model fit object:
## parsnip model object
##
## ##### xgb.Booster
## raw: 51.4 Kb
## call:
## xgboost::xgb.train(params = list(eta = 0.3, max_depth = 6, gamma = 0,
## colsample_bytree = 1, colsample_bynode = 1, min_child_weight = 1,
## subsample = 1), data = x$data, nrounds = 15, watchlist = x$watchlist,
## verbose = 0, nthread = 1, objective = "reg:squarederror")
## params (as set within xgb.train):
## eta = "0.3", max_depth = "6", gamma = "0", colsample_bytree = "1", colsample_bynode = "1", min_child_weight = "1", subsample = "1", nthread = "1", objective = "reg:squarederror", validate_parameters = "TRUE"
## xgb.attributes:
## niter
## callbacks:
## cb.evaluation.log()
## # of features: 2
## niter: 15
## nfeatures : 2
## evaluation_log:
## iter training_rmse
## 1 10.481475
## 2 7.620929
## ---
## 14 2.551943
## 15 2.531085The holdout data can be predicted:
predict(bt_reg_fit, Chicago_test) ## # A tibble: 7 × 1
## .pred
## <dbl>
## 1 20.6
## 2 20.6
## 3 20.2
## 4 20.6
## 5 19.3
## 6 7.26
## 7 5.92
Classification Example (xgboost)
The example data has two predictors and an outcome with two classes. Both predictors are in the same units.
library(tidymodels)
tidymodels_prefer()
data(two_class_dat)
data_train <- two_class_dat[-(1:10), ]
data_test <- two_class_dat[ 1:10 , ]We can define the model with specific parameters:
bt_cls_spec <-
boost_tree(trees = 15) %>%
# This model can be used for classification or regression, so set mode
set_mode("classification") %>%
set_engine("xgboost")
bt_cls_spec ## Boosted Tree Model Specification (classification)
##
## Main Arguments:
## trees = 15
##
## Computational engine: xgboostNow we create the model fit object:
## parsnip model object
##
## ##### xgb.Booster
## raw: 40.8 Kb
## call:
## xgboost::xgb.train(params = list(eta = 0.3, max_depth = 6, gamma = 0,
## colsample_bytree = 1, colsample_bynode = 1, min_child_weight = 1,
## subsample = 1), data = x$data, nrounds = 15, watchlist = x$watchlist,
## verbose = 0, nthread = 1, objective = "binary:logistic")
## params (as set within xgb.train):
## eta = "0.3", max_depth = "6", gamma = "0", colsample_bytree = "1", colsample_bynode = "1", min_child_weight = "1", subsample = "1", nthread = "1", objective = "binary:logistic", validate_parameters = "TRUE"
## xgb.attributes:
## niter
## callbacks:
## cb.evaluation.log()
## # of features: 2
## niter: 15
## nfeatures : 2
## evaluation_log:
## iter training_logloss
## 1 0.5524619
## 2 0.4730697
## ---
## 14 0.2523133
## 15 0.2490712The holdout data can be predicted for both hard class predictions and probabilities. We’ll bind these together into one tibble:
## # A tibble: 10 × 3
## .pred_class .pred_Class1 .pred_Class2
## <fct> <dbl> <dbl>
## 1 Class2 0.220 0.780
## 2 Class1 0.931 0.0689
## 3 Class1 0.638 0.362
## 4 Class1 0.815 0.185
## 5 Class2 0.292 0.708
## 6 Class2 0.120 0.880
## 7 Class1 0.796 0.204
## 8 Class2 0.392 0.608
## 9 Class1 0.879 0.121
## 10 Class2 0.0389 0.961
With the "C5.0" engine
Classification Example (C5.0)
The example data has two predictors and an outcome with two classes. Both predictors are in the same units.
library(tidymodels)
tidymodels_prefer()
data(two_class_dat)
data_train <- two_class_dat[-(1:10), ]
data_test <- two_class_dat[ 1:10 , ]We can define the model with specific parameters:
bt_cls_spec <-
boost_tree(trees = 15) %>%
set_mode("classification") %>%
set_engine("C5.0")
bt_cls_spec ## Boosted Tree Model Specification (classification)
##
## Main Arguments:
## trees = 15
##
## Computational engine: C5.0Now we create the model fit object:
## parsnip model object
##
##
## Call:
## C5.0.default(x = x, y = y, trials = 15, control
## = C50::C5.0Control(minCases = 2, sample = 0))
##
## Classification Tree
## Number of samples: 781
## Number of predictors: 2
##
## Number of boosting iterations: 15 requested; 6 used due to early stopping
## Average tree size: 3.2
##
## Non-standard options: attempt to group attributesThe holdout data can be predicted for both hard class predictions and probabilities. We’ll bind these together into one tibble:
## # A tibble: 10 × 3
## .pred_class .pred_Class1 .pred_Class2
## <fct> <dbl> <dbl>
## 1 Class2 0.311 0.689
## 2 Class1 0.863 0.137
## 3 Class1 0.535 0.465
## 4 Class2 0.336 0.664
## 5 Class2 0.336 0.664
## 6 Class2 0.137 0.863
## 7 Class2 0.496 0.504
## 8 Class2 0.311 0.689
## 9 Class1 1 0
## 10 Class2 0 1
decision_tree() models
With the "rpart" engine
Regression Example (rpart)
We’ll model the ridership on the Chicago elevated trains as a function of the 14 day lagged ridership at two stations. The two predictors are in the same units (rides per day/1000) and do not need to be normalized. All but the last week of data are used for training. The last week will be predicted after the model is fit.
library(tidymodels)
tidymodels_prefer()
data(Chicago)
n <- nrow(Chicago)
Chicago <- Chicago %>% select(ridership, Clark_Lake, Quincy_Wells)
Chicago_train <- Chicago[1:(n - 7), ]
Chicago_test <- Chicago[(n - 6):n, ]We can define the model with specific parameters:
dt_reg_spec <-
decision_tree(tree_depth = 30) %>%
# This model can be used for classification or regression, so set mode
set_mode("regression") %>%
set_engine("rpart")
dt_reg_spec ## Decision Tree Model Specification (regression)
##
## Main Arguments:
## tree_depth = 30
##
## Computational engine: rpartNow we create the model fit object:
## parsnip model object
##
## n= 5691
##
## node), split, n, deviance, yval
## * denotes terminal node
##
## 1) root 5691 244958.800 13.615560
## 2) Quincy_Wells< 2.737 1721 22973.630 5.194394
## 4) Clark_Lake< 5.07 1116 13166.830 4.260215 *
## 5) Clark_Lake>=5.07 605 7036.349 6.917607 *
## 3) Quincy_Wells>=2.737 3970 47031.540 17.266140
## 6) Clark_Lake< 17.6965 1940 16042.090 15.418210 *
## 7) Clark_Lake>=17.6965 2030 18033.560 19.032140 *The holdout data can be predicted:
predict(dt_reg_fit, Chicago_test) ## # A tibble: 7 × 1
## .pred
## <dbl>
## 1 19.0
## 2 19.0
## 3 19.0
## 4 19.0
## 5 19.0
## 6 6.92
## 7 6.92
Classification Example (rpart)
The example data has two predictors and an outcome with two classes. Both predictors are in the same units.
library(tidymodels)
tidymodels_prefer()
data(two_class_dat)
data_train <- two_class_dat[-(1:10), ]
data_test <- two_class_dat[ 1:10 , ]We can define the model with specific parameters:
dt_cls_spec <-
decision_tree(tree_depth = 30) %>%
# This model can be used for classification or regression, so set mode
set_mode("classification") %>%
set_engine("rpart")
dt_cls_spec ## Decision Tree Model Specification (classification)
##
## Main Arguments:
## tree_depth = 30
##
## Computational engine: rpartNow we create the model fit object:
## parsnip model object
##
## n= 781
##
## node), split, n, loss, yval, (yprob)
## * denotes terminal node
##
## 1) root 781 348 Class1 (0.5544174 0.4455826)
## 2) B< 1.495535 400 61 Class1 (0.8475000 0.1525000) *
## 3) B>=1.495535 381 94 Class2 (0.2467192 0.7532808)
## 6) B< 2.079458 191 70 Class2 (0.3664921 0.6335079)
## 12) A>=2.572663 48 13 Class1 (0.7291667 0.2708333) *
## 13) A< 2.572663 143 35 Class2 (0.2447552 0.7552448) *
## 7) B>=2.079458 190 24 Class2 (0.1263158 0.8736842) *The holdout data can be predicted for both hard class predictions and probabilities. We’ll bind these together into one tibble:
## # A tibble: 10 × 3
## .pred_class .pred_Class1 .pred_Class2
## <fct> <dbl> <dbl>
## 1 Class2 0.245 0.755
## 2 Class1 0.848 0.152
## 3 Class1 0.848 0.152
## 4 Class1 0.729 0.271
## 5 Class1 0.729 0.271
## 6 Class2 0.126 0.874
## 7 Class2 0.245 0.755
## 8 Class2 0.245 0.755
## 9 Class1 0.848 0.152
## 10 Class2 0.126 0.874
With the "C5.0" engine
Classification Example (C5.0)
The example data has two predictors and an outcome with two classes. Both predictors are in the same units.
library(tidymodels)
tidymodels_prefer()
data(two_class_dat)
data_train <- two_class_dat[-(1:10), ]
data_test <- two_class_dat[ 1:10 , ]We can define the model with specific parameters:
dt_cls_spec <-
decision_tree(min_n = 2) %>%
set_mode("classification") %>%
set_engine("C5.0")
dt_cls_spec ## Decision Tree Model Specification (classification)
##
## Main Arguments:
## min_n = 2
##
## Computational engine: C5.0Now we create the model fit object:
## parsnip model object
##
##
## Call:
## C5.0.default(x = x, y = y, trials = 1, control
## = C50::C5.0Control(minCases = 2, sample = 0))
##
## Classification Tree
## Number of samples: 781
## Number of predictors: 2
##
## Tree size: 4
##
## Non-standard options: attempt to group attributesThe holdout data can be predicted for both hard class predictions and probabilities. We’ll bind these together into one tibble:
## # A tibble: 10 × 3
## .pred_class .pred_Class1 .pred_Class2
## <fct> <dbl> <dbl>
## 1 Class2 0.233 0.767
## 2 Class1 0.847 0.153
## 3 Class1 0.847 0.153
## 4 Class1 0.727 0.273
## 5 Class1 0.727 0.273
## 6 Class2 0.118 0.882
## 7 Class2 0.233 0.767
## 8 Class2 0.233 0.767
## 9 Class1 0.847 0.153
## 10 Class2 0.118 0.882
gen_additive_mod() models
With the "mgcv" engine
Regression Example (mgcv)
We’ll model the ridership on the Chicago elevated trains as a function of the 14 day lagged ridership at two stations. The two predictors are in the same units (rides per day/1000) and do not need to be normalized. All but the last week of data are used for training. The last week will be predicted after the model is fit.
library(tidymodels)
tidymodels_prefer()
data(Chicago)
n <- nrow(Chicago)
Chicago <- Chicago %>% select(ridership, Clark_Lake, Quincy_Wells)
Chicago_train <- Chicago[1:(n - 7), ]
Chicago_test <- Chicago[(n - 6):n, ]We can define the model with specific parameters:
gam_reg_spec <-
gen_additive_mod(select_features = FALSE, adjust_deg_free = 10) %>%
# This model can be used for classification or regression, so set mode
set_mode("regression") %>%
set_engine("mgcv")
gam_reg_spec ## GAM Model Specification (regression)
##
## Main Arguments:
## select_features = FALSE
## adjust_deg_free = 10
##
## Computational engine: mgcvNow we create the model fit object:
set.seed(1)
gam_reg_fit <- gam_reg_spec %>%
fit(ridership ~ Clark_Lake + Quincy_Wells, data = Chicago_train)
gam_reg_fit ## parsnip model object
##
##
## Family: gaussian
## Link function: identity
##
## Formula:
## ridership ~ Clark_Lake + Quincy_Wells
## Total model degrees of freedom 3
##
## GCV score: 9.505245The holdout data can be predicted:
predict(gam_reg_fit, Chicago_test) ## # A tibble: 7 × 1
## .pred
## <dbl>
## 1 20.3
## 2 20.5
## 3 20.8
## 4 20.5
## 5 18.8
## 6 7.45
## 7 7.02
Classification Example (mgcv)
The example data has two predictors and an outcome with two classes. Both predictors are in the same units.
library(tidymodels)
tidymodels_prefer()
data(two_class_dat)
data_train <- two_class_dat[-(1:10), ]
data_test <- two_class_dat[ 1:10 , ]We can define the model with specific parameters:
gam_cls_spec <-
gen_additive_mod(select_features = FALSE, adjust_deg_free = 10) %>%
# This model can be used for classification or regression, so set mode
set_mode("classification") %>%
set_engine("mgcv")
gam_cls_spec ## GAM Model Specification (classification)
##
## Main Arguments:
## select_features = FALSE
## adjust_deg_free = 10
##
## Computational engine: mgcvNow we create the model fit object:
## parsnip model object
##
##
## Family: binomial
## Link function: logit
##
## Formula:
## Class ~ A + B
## Total model degrees of freedom 3
##
## UBRE score: -0.07548008The holdout data can be predicted for both hard class predictions and probabilities. We’ll bind these together into one tibble:
## # A tibble: 10 × 3
## .pred_class .pred_Class1 .pred_Class2
## <fct> <dbl> <dbl>
## 1 Class1 0.518 0.482
## 2 Class1 0.909 0.0913
## 3 Class1 0.648 0.352
## 4 Class1 0.610 0.390
## 5 Class2 0.443 0.557
## 6 Class2 0.206 0.794
## 7 Class1 0.708 0.292
## 8 Class1 0.567 0.433
## 9 Class1 0.994 0.00582
## 10 Class2 0.108 0.892
linear_reg() models
With the "lm" engine
Regression Example (lm)
We’ll model the ridership on the Chicago elevated trains as a function of the 14 day lagged ridership at two stations. The two predictors are in the same units (rides per day/1000) and do not need to be normalized. All but the last week of data are used for training. The last week will be predicted after the model is fit.
library(tidymodels)
tidymodels_prefer()
data(Chicago)
n <- nrow(Chicago)
Chicago <- Chicago %>% select(ridership, Clark_Lake, Quincy_Wells)
Chicago_train <- Chicago[1:(n - 7), ]
Chicago_test <- Chicago[(n - 6):n, ]We can define the model with specific parameters:
linreg_reg_spec <-
linear_reg() %>%
set_engine("lm")
linreg_reg_spec ## Linear Regression Model Specification (regression)
##
## Computational engine: lmNow we create the model fit object:
set.seed(1)
linreg_reg_fit <- linreg_reg_spec %>% fit(ridership ~ ., data = Chicago_train)
linreg_reg_fit ## parsnip model object
##
##
## Call:
## stats::lm(formula = ridership ~ ., data = data)
##
## Coefficients:
## (Intercept) Clark_Lake Quincy_Wells
## 1.6624 0.7738 0.2557The holdout data can be predicted:
predict(linreg_reg_fit, Chicago_test) ## # A tibble: 7 × 1
## .pred
## <dbl>
## 1 20.3
## 2 20.5
## 3 20.8
## 4 20.5
## 5 18.8
## 6 7.45
## 7 7.02
With the "glm" engine
Regression Example (glm)
We’ll model the ridership on the Chicago elevated trains as a function of the 14 day lagged ridership at two stations. The two predictors are in the same units (rides per day/1000) and do not need to be normalized. All but the last week of data are used for training. The last week will be predicted after the model is fit.
library(tidymodels)
tidymodels_prefer()
data(Chicago)
n <- nrow(Chicago)
Chicago <- Chicago %>% select(ridership, Clark_Lake, Quincy_Wells)
Chicago_train <- Chicago[1:(n - 7), ]
Chicago_test <- Chicago[(n - 6):n, ]We can define the model with specific parameters:
linreg_reg_spec <-
linear_reg() %>%
set_engine("glm")
linreg_reg_spec ## Linear Regression Model Specification (regression)
##
## Computational engine: glmNow we create the model fit object:
set.seed(1)
linreg_reg_fit <- linreg_reg_spec %>% fit(ridership ~ ., data = Chicago_train)
linreg_reg_fit ## parsnip model object
##
##
## Call: stats::glm(formula = ridership ~ ., family = stats::gaussian,
## data = data)
##
## Coefficients:
## (Intercept) Clark_Lake Quincy_Wells
## 1.6624 0.7738 0.2557
##
## Degrees of Freedom: 5690 Total (i.e. Null); 5688 Residual
## Null Deviance: 245000
## Residual Deviance: 53530 AIC: 28910The holdout data can be predicted:
predict(linreg_reg_fit, Chicago_test) ## # A tibble: 7 × 1
## .pred
## <dbl>
## 1 20.3
## 2 20.5
## 3 20.8
## 4 20.5
## 5 18.8
## 6 7.45
## 7 7.02
With the "glmnet" engine
Regression Example (glmnet)
We’ll model the ridership on the Chicago elevated trains as a function of the 14 day lagged ridership at two stations. The two predictors are in the same units (rides per day/1000) and do not need to be normalized. All but the last week of data are used for training. The last week will be predicted after the model is fit.
library(tidymodels)
tidymodels_prefer()
data(Chicago)
n <- nrow(Chicago)
Chicago <- Chicago %>% select(ridership, Clark_Lake, Quincy_Wells)
Chicago_train <- Chicago[1:(n - 7), ]
Chicago_test <- Chicago[(n - 6):n, ]We can define the model with specific parameters:
linreg_reg_spec <-
linear_reg(penalty = 0.1) %>%
set_engine("glmnet")
linreg_reg_spec ## Linear Regression Model Specification (regression)
##
## Main Arguments:
## penalty = 0.1
##
## Computational engine: glmnetNow we create the model fit object:
set.seed(1)
linreg_reg_fit <- linreg_reg_spec %>% fit(ridership ~ ., data = Chicago_train)
linreg_reg_fit ## parsnip model object
##
##
## Call: glmnet::glmnet(x = maybe_matrix(x), y = y, family = "gaussian")
##
## Df %Dev Lambda
## 1 0 0.00 5.7970
## 2 1 13.25 5.2820
## 3 1 24.26 4.8130
## 4 1 33.40 4.3850
## 5 1 40.98 3.9960
## 6 1 47.28 3.6410
## 7 1 52.51 3.3170
## 8 1 56.85 3.0220
## 9 1 60.45 2.7540
## 10 1 63.44 2.5090
## 11 1 65.92 2.2860
## 12 1 67.99 2.0830
## 13 1 69.70 1.8980
## 14 1 71.12 1.7300
## 15 1 72.30 1.5760
## 16 2 73.29 1.4360
## 17 2 74.11 1.3080
## 18 2 74.80 1.1920
## 19 2 75.37 1.0860
## 20 2 75.84 0.9897
## 21 2 76.23 0.9018
## 22 2 76.56 0.8217
## 23 2 76.83 0.7487
## 24 2 77.05 0.6822
## 25 2 77.24 0.6216
## 26 2 77.39 0.5664
## 27 2 77.52 0.5160
## 28 2 77.63 0.4702
## 29 2 77.72 0.4284
## 30 2 77.79 0.3904
## 31 2 77.85 0.3557
## 32 2 77.90 0.3241
## 33 2 77.94 0.2953
## 34 2 77.98 0.2691
## 35 2 78.01 0.2452
## 36 2 78.03 0.2234
## 37 2 78.05 0.2035
## 38 2 78.07 0.1855
## 39 2 78.08 0.1690
## 40 2 78.09 0.1540
## 41 2 78.10 0.1403
## 42 2 78.11 0.1278
## 43 2 78.12 0.1165
## 44 2 78.12 0.1061
## 45 2 78.13 0.0967
## 46 2 78.13 0.0881
## 47 2 78.13 0.0803
## 48 2 78.14 0.0732
## 49 2 78.14 0.0666
## 50 2 78.14 0.0607
## 51 2 78.14 0.0553
## 52 2 78.14 0.0504
## 53 2 78.14 0.0459
## 54 2 78.15 0.0419
## 55 2 78.15 0.0381The holdout data can be predicted:
predict(linreg_reg_fit, Chicago_test) ## # A tibble: 7 × 1
## .pred
## <dbl>
## 1 20.2
## 2 20.4
## 3 20.7
## 4 20.4
## 5 18.7
## 6 7.57
## 7 7.15
With the "keras" engine
Regression Example (keras)
We’ll model the ridership on the Chicago elevated trains as a function of the 14 day lagged ridership at two stations. The two predictors are in the same units (rides per day/1000) and do not need to be normalized. All but the last week of data are used for training. The last week will be predicted after the model is fit.
library(tidymodels)
tidymodels_prefer()
data(Chicago)
n <- nrow(Chicago)
Chicago <- Chicago %>% select(ridership, Clark_Lake, Quincy_Wells)
Chicago_train <- Chicago[1:(n - 7), ]
Chicago_test <- Chicago[(n - 6):n, ]We can define the model with specific parameters:
linreg_reg_spec <-
linear_reg(penalty = 0.1) %>%
set_engine("keras")
linreg_reg_spec ## Linear Regression Model Specification (regression)
##
## Main Arguments:
## penalty = 0.1
##
## Computational engine: kerasNow we create the model fit object:
## Epoch 1/20
##
1/178 [..............................] - ETA: 54s - loss: 12.1923
43/178 [======>.......................] - ETA: 0s - loss: 10.4814
86/178 [=============>................] - ETA: 0s - loss: 10.2096
131/178 [=====================>........] - ETA: 0s - loss: 9.9702
177/178 [============================>.] - ETA: 0s - loss: 9.9890
178/178 [==============================] - 1s 1ms/step - loss: 10.0220
## Epoch 2/20
##
1/178 [..............................] - ETA: 0s - loss: 15.2292
47/178 [======>.......................] - ETA: 0s - loss: 9.0211
91/178 [==============>...............] - ETA: 0s - loss: 9.3685
137/178 [======================>.......] - ETA: 0s - loss: 9.6647
178/178 [==============================] - 0s 1ms/step - loss: 9.9143
## Epoch 3/20
##
1/178 [..............................] - ETA: 0s - loss: 11.8143
49/178 [=======>......................] - ETA: 0s - loss: 10.1236
89/178 [==============>...............] - ETA: 0s - loss: 9.9415
127/178 [====================>.........] - ETA: 0s - loss: 9.5095
166/178 [==========================>...] - ETA: 0s - loss: 9.8967
178/178 [==============================] - 0s 1ms/step - loss: 9.8424
## Epoch 4/20
##
1/178 [..............................] - ETA: 0s - loss: 10.8115
44/178 [======>.......................] - ETA: 0s - loss: 9.8545
86/178 [=============>................] - ETA: 0s - loss: 9.4829
129/178 [====================>.........] - ETA: 0s - loss: 9.4390
166/178 [==========================>...] - ETA: 0s - loss: 9.6763
178/178 [==============================] - 0s 1ms/step - loss: 9.7814
## Epoch 5/20
##
1/178 [..............................] - ETA: 0s - loss: 11.0716
43/178 [======>.......................] - ETA: 0s - loss: 10.2221
89/178 [==============>...............] - ETA: 0s - loss: 10.3782
136/178 [=====================>........] - ETA: 0s - loss: 9.9455
178/178 [==============================] - 0s 1ms/step - loss: 9.7335
## Epoch 6/20
##
1/178 [..............................] - ETA: 0s - loss: 1.9127
46/178 [======>.......................] - ETA: 0s - loss: 10.0942
88/178 [=============>................] - ETA: 0s - loss: 9.1450
130/178 [====================>.........] - ETA: 0s - loss: 9.5175
171/178 [===========================>..] - ETA: 0s - loss: 9.6946
178/178 [==============================] - 0s 1ms/step - loss: 9.6985
## Epoch 7/20
##
1/178 [..............................] - ETA: 0s - loss: 12.6962
47/178 [======>.......................] - ETA: 0s - loss: 8.5845
92/178 [==============>...............] - ETA: 0s - loss: 9.3087
142/178 [======================>.......] - ETA: 0s - loss: 9.7268
178/178 [==============================] - 0s 1ms/step - loss: 9.6696
## Epoch 8/20
##
1/178 [..............................] - ETA: 0s - loss: 3.8956
48/178 [=======>......................] - ETA: 0s - loss: 11.0778
91/178 [==============>...............] - ETA: 0s - loss: 9.7767
136/178 [=====================>........] - ETA: 0s - loss: 9.9041
178/178 [==============================] - 0s 1ms/step - loss: 9.6376
## Epoch 9/20
##
1/178 [..............................] - ETA: 0s - loss: 20.0554
45/178 [======>.......................] - ETA: 0s - loss: 10.2998
86/178 [=============>................] - ETA: 0s - loss: 10.2915
128/178 [====================>.........] - ETA: 0s - loss: 9.9247
171/178 [===========================>..] - ETA: 0s - loss: 9.4617
178/178 [==============================] - 0s 1ms/step - loss: 9.6140
## Epoch 10/20
##
1/178 [..............................] - ETA: 0s - loss: 14.4421
50/178 [=======>......................] - ETA: 0s - loss: 10.1294
85/178 [=============>................] - ETA: 0s - loss: 9.9561
128/178 [====================>.........] - ETA: 0s - loss: 9.6716
174/178 [============================>.] - ETA: 0s - loss: 9.5398
178/178 [==============================] - 0s 1ms/step - loss: 9.6002
## Epoch 11/20
##
1/178 [..............................] - ETA: 0s - loss: 21.8872
49/178 [=======>......................] - ETA: 0s - loss: 10.3087
95/178 [===============>..............] - ETA: 0s - loss: 9.9672
140/178 [======================>.......] - ETA: 0s - loss: 9.4114
178/178 [==============================] - 0s 1ms/step - loss: 9.5852
## Epoch 12/20
##
1/178 [..............................] - ETA: 0s - loss: 19.5860
45/178 [======>.......................] - ETA: 0s - loss: 9.6436
94/178 [==============>...............] - ETA: 0s - loss: 9.3453
141/178 [======================>.......] - ETA: 0s - loss: 9.3497
178/178 [==============================] - 0s 1ms/step - loss: 9.5722
## Epoch 13/20
##
1/178 [..............................] - ETA: 0s - loss: 32.2263
48/178 [=======>......................] - ETA: 0s - loss: 8.9993
95/178 [===============>..............] - ETA: 0s - loss: 9.9084
141/178 [======================>.......] - ETA: 0s - loss: 9.6362
178/178 [==============================] - 0s 1ms/step - loss: 9.5705
## Epoch 14/20
##
1/178 [..............................] - ETA: 0s - loss: 3.0004
49/178 [=======>......................] - ETA: 0s - loss: 8.8922
95/178 [===============>..............] - ETA: 0s - loss: 8.6795
144/178 [=======================>......] - ETA: 0s - loss: 9.2456
178/178 [==============================] - 0s 1ms/step - loss: 9.5505
## Epoch 15/20
##
1/178 [..............................] - ETA: 0s - loss: 6.5045
49/178 [=======>......................] - ETA: 0s - loss: 9.9139
97/178 [===============>..............] - ETA: 0s - loss: 9.3912
147/178 [=======================>......] - ETA: 0s - loss: 9.5402
178/178 [==============================] - 0s 1ms/step - loss: 9.5569
## Epoch 16/20
##
1/178 [..............................] - ETA: 0s - loss: 29.7768
48/178 [=======>......................] - ETA: 0s - loss: 10.7914
95/178 [===============>..............] - ETA: 0s - loss: 9.8099
139/178 [======================>.......] - ETA: 0s - loss: 9.4603
178/178 [==============================] - 0s 1ms/step - loss: 9.5307
## Epoch 17/20
##
1/178 [..............................] - ETA: 0s - loss: 9.9179
48/178 [=======>......................] - ETA: 0s - loss: 9.4387
99/178 [===============>..............] - ETA: 0s - loss: 9.3854
148/178 [=======================>......] - ETA: 0s - loss: 9.6997
178/178 [==============================] - 0s 1ms/step - loss: 9.5417
## Epoch 18/20
##
1/178 [..............................] - ETA: 0s - loss: 4.8675
45/178 [======>.......................] - ETA: 0s - loss: 9.8833
91/178 [==============>...............] - ETA: 0s - loss: 9.4714
136/178 [=====================>........] - ETA: 0s - loss: 9.7495
178/178 [==============================] - 0s 1ms/step - loss: 9.5303
## Epoch 19/20
##
1/178 [..............................] - ETA: 0s - loss: 8.6884
47/178 [======>.......................] - ETA: 0s - loss: 10.5975
85/178 [=============>................] - ETA: 0s - loss: 10.5806
127/178 [====================>.........] - ETA: 0s - loss: 9.6541
170/178 [===========================>..] - ETA: 0s - loss: 9.5569
178/178 [==============================] - 0s 1ms/step - loss: 9.5212
## Epoch 20/20
##
1/178 [..............................] - ETA: 0s - loss: 7.9026
35/178 [====>.........................] - ETA: 0s - loss: 10.2927
79/178 [============>.................] - ETA: 0s - loss: 9.5031
123/178 [===================>..........] - ETA: 0s - loss: 9.8590
159/178 [=========================>....] - ETA: 0s - loss: 9.6242
178/178 [==============================] - 0s 1ms/step - loss: 9.5202
linreg_reg_fit ## parsnip model object
##
## Model: "sequential"
## __________________________________________________________________________
## Layer (type) Output Shape Param #
## ==========================================================================
## dense (Dense) (None, 1) 3
## dense_1 (Dense) (None, 1) 2
## ==========================================================================
## Total params: 5
## Trainable params: 5
## Non-trainable params: 0
## __________________________________________________________________________The holdout data can be predicted:
predict(linreg_reg_fit, Chicago_test) ## # A tibble: 7 × 1
## .pred
## <dbl>
## 1 20.4
## 2 20.7
## 3 21.0
## 4 20.7
## 5 19.0
## 6 7.46
## 7 7.09
With the "stan" engine
Regression Example (stan)
We’ll model the ridership on the Chicago elevated trains as a function of the 14 day lagged ridership at two stations. The two predictors are in the same units (rides per day/1000) and do not need to be normalized. All but the last week of data are used for training. The last week will be predicted after the model is fit.
library(tidymodels)
tidymodels_prefer()
data(Chicago)
n <- nrow(Chicago)
Chicago <- Chicago %>% select(ridership, Clark_Lake, Quincy_Wells)
Chicago_train <- Chicago[1:(n - 7), ]
Chicago_test <- Chicago[(n - 6):n, ]We can define the model with specific parameters:
linreg_reg_spec <-
linear_reg() %>%
set_engine("stan")
linreg_reg_spec ## Linear Regression Model Specification (regression)
##
## Computational engine: stanNow we create the model fit object:
set.seed(1)
linreg_reg_fit <- linreg_reg_spec %>% fit(ridership ~ ., data = Chicago_train)
linreg_reg_fit ## parsnip model object
##
## stan_glm
## family: gaussian [identity]
## formula: ridership ~ .
## observations: 5691
## predictors: 3
## ------
## Median MAD_SD
## (Intercept) 1.7 0.1
## Clark_Lake 0.8 0.0
## Quincy_Wells 0.3 0.1
##
## Auxiliary parameter(s):
## Median MAD_SD
## sigma 3.1 0.0
##
## ------
## * For help interpreting the printed output see ?print.stanreg
## * For info on the priors used see ?prior_summary.stanregThe holdout data can be predicted:
predict(linreg_reg_fit, Chicago_test) ## # A tibble: 7 × 1
## .pred
## <dbl>
## 1 20.3
## 2 20.5
## 3 20.8
## 4 20.5
## 5 18.8
## 6 7.45
## 7 7.02
logistic_reg() models
With the "glm" engine
Classification Example (glm)
The example data has two predictors and an outcome with two classes. Both predictors are in the same units.
library(tidymodels)
tidymodels_prefer()
data(two_class_dat)
data_train <- two_class_dat[-(1:10), ]
data_test <- two_class_dat[ 1:10 , ]We can define the model with specific parameters:
logreg_cls_spec <-
logistic_reg() %>%
set_engine("glm")
logreg_cls_spec ## Logistic Regression Model Specification (classification)
##
## Computational engine: glmNow we create the model fit object:
## parsnip model object
##
##
## Call: stats::glm(formula = Class ~ ., family = stats::binomial, data = data)
##
## Coefficients:
## (Intercept) A B
## -3.755 -1.259 3.855
##
## Degrees of Freedom: 780 Total (i.e. Null); 778 Residual
## Null Deviance: 1073
## Residual Deviance: 662.1 AIC: 668.1The holdout data can be predicted for both hard class predictions and probabilities. We’ll bind these together into one tibble:
## # A tibble: 10 × 3
## .pred_class .pred_Class1 .pred_Class2
## <fct> <dbl> <dbl>
## 1 Class1 0.518 0.482
## 2 Class1 0.909 0.0913
## 3 Class1 0.648 0.352
## 4 Class1 0.610 0.390
## 5 Class2 0.443 0.557
## 6 Class2 0.206 0.794
## 7 Class1 0.708 0.292
## 8 Class1 0.567 0.433
## 9 Class1 0.994 0.00582
## 10 Class2 0.108 0.892
With the "glmnet" engine
Classification Example (glmnet)
The example data has two predictors and an outcome with two classes. Both predictors are in the same units.
library(tidymodels)
tidymodels_prefer()
data(two_class_dat)
data_train <- two_class_dat[-(1:10), ]
data_test <- two_class_dat[ 1:10 , ]We can define the model with specific parameters:
logreg_cls_spec <-
logistic_reg(penalty = 0.1) %>%
set_engine("glmnet")
logreg_cls_spec ## Logistic Regression Model Specification (classification)
##
## Main Arguments:
## penalty = 0.1
##
## Computational engine: glmnetNow we create the model fit object:
## parsnip model object
##
##
## Call: glmnet::glmnet(x = maybe_matrix(x), y = y, family = "binomial")
##
## Df %Dev Lambda
## 1 0 0.00 0.308500
## 2 1 4.76 0.281100
## 3 1 8.75 0.256100
## 4 1 12.13 0.233300
## 5 1 15.01 0.212600
## 6 1 17.50 0.193700
## 7 1 19.64 0.176500
## 8 1 21.49 0.160800
## 9 1 23.10 0.146500
## 10 1 24.49 0.133500
## 11 1 25.71 0.121700
## 12 1 26.76 0.110900
## 13 1 27.67 0.101000
## 14 1 28.46 0.092030
## 15 1 29.15 0.083860
## 16 1 29.74 0.076410
## 17 1 30.25 0.069620
## 18 1 30.70 0.063430
## 19 1 31.08 0.057800
## 20 1 31.40 0.052660
## 21 1 31.68 0.047990
## 22 1 31.92 0.043720
## 23 1 32.13 0.039840
## 24 2 32.70 0.036300
## 25 2 33.50 0.033070
## 26 2 34.18 0.030140
## 27 2 34.78 0.027460
## 28 2 35.29 0.025020
## 29 2 35.72 0.022800
## 30 2 36.11 0.020770
## 31 2 36.43 0.018930
## 32 2 36.71 0.017250
## 33 2 36.96 0.015710
## 34 2 37.16 0.014320
## 35 2 37.34 0.013050
## 36 2 37.49 0.011890
## 37 2 37.62 0.010830
## 38 2 37.73 0.009868
## 39 2 37.82 0.008992
## 40 2 37.90 0.008193
## 41 2 37.97 0.007465
## 42 2 38.02 0.006802
## 43 2 38.07 0.006198
## 44 2 38.11 0.005647
## 45 2 38.15 0.005145
## 46 2 38.18 0.004688
## 47 2 38.20 0.004272
## 48 2 38.22 0.003892
## 49 2 38.24 0.003547
## 50 2 38.25 0.003231
## 51 2 38.26 0.002944
## 52 2 38.27 0.002683
## 53 2 38.28 0.002444
## 54 2 38.29 0.002227
## 55 2 38.29 0.002029
## 56 2 38.30 0.001849
## 57 2 38.30 0.001685
## 58 2 38.31 0.001535
## 59 2 38.31 0.001399
## 60 2 38.31 0.001275
## 61 2 38.31 0.001161
## 62 2 38.32 0.001058
## 63 2 38.32 0.000964
## 64 2 38.32 0.000879
## 65 2 38.32 0.000800The holdout data can be predicted for both hard class predictions and probabilities. We’ll bind these together into one tibble:
## # A tibble: 10 × 3
## .pred_class .pred_Class1 .pred_Class2
## <fct> <dbl> <dbl>
## 1 Class1 0.530 0.470
## 2 Class1 0.713 0.287
## 3 Class1 0.616 0.384
## 4 Class2 0.416 0.584
## 5 Class2 0.417 0.583
## 6 Class2 0.288 0.712
## 7 Class1 0.554 0.446
## 8 Class1 0.557 0.443
## 9 Class1 0.820 0.180
## 10 Class2 0.206 0.794
With the "keras" engine
Classification Example (keras)
The example data has two predictors and an outcome with two classes. Both predictors are in the same units.
library(tidymodels)
tidymodels_prefer()
data(two_class_dat)
data_train <- two_class_dat[-(1:10), ]
data_test <- two_class_dat[ 1:10 , ]We can define the model with specific parameters:
logreg_cls_spec <-
logistic_reg(penalty = 0.1) %>%
set_engine("keras")
logreg_cls_spec ## Logistic Regression Model Specification (classification)
##
## Main Arguments:
## penalty = 0.1
##
## Computational engine: kerasNow we create the model fit object:
## Epoch 1/20
##
1/25 [>.............................] - ETA: 10s - loss: 0.9082
25/25 [==============================] - 0s 2ms/step - loss: 0.8996
## Epoch 2/20
##
1/25 [>.............................] - ETA: 0s - loss: 0.9254
25/25 [==============================] - 0s 1ms/step - loss: 0.8888
## Epoch 3/20
##
1/25 [>.............................] - ETA: 0s - loss: 0.9751
25/25 [==============================] - 0s 1ms/step - loss: 0.8787
## Epoch 4/20
##
1/25 [>.............................] - ETA: 0s - loss: 0.8901
25/25 [==============================] - 0s 1ms/step - loss: 0.8684
## Epoch 5/20
##
1/25 [>.............................] - ETA: 0s - loss: 0.8682
25/25 [==============================] - 0s 1ms/step - loss: 0.8585
## Epoch 6/20
##
1/25 [>.............................] - ETA: 0s - loss: 0.8751
25/25 [==============================] - 0s 1ms/step - loss: 0.8486
## Epoch 7/20
##
1/25 [>.............................] - ETA: 0s - loss: 0.8443
25/25 [==============================] - 0s 1ms/step - loss: 0.8388
## Epoch 8/20
##
1/25 [>.............................] - ETA: 0s - loss: 0.8296
25/25 [==============================] - 0s 1ms/step - loss: 0.8292
## Epoch 9/20
##
1/25 [>.............................] - ETA: 0s - loss: 0.7735
25/25 [==============================] - 0s 1ms/step - loss: 0.8198
## Epoch 10/20
##
1/25 [>.............................] - ETA: 0s - loss: 0.7891
25/25 [==============================] - 0s 1ms/step - loss: 0.8107
## Epoch 11/20
##
1/25 [>.............................] - ETA: 0s - loss: 0.8162
25/25 [==============================] - 0s 1ms/step - loss: 0.8017
## Epoch 12/20
##
1/25 [>.............................] - ETA: 0s - loss: 0.7737
25/25 [==============================] - 0s 1ms/step - loss: 0.7928
## Epoch 13/20
##
1/25 [>.............................] - ETA: 0s - loss: 0.7820
25/25 [==============================] - 0s 1ms/step - loss: 0.7841
## Epoch 14/20
##
1/25 [>.............................] - ETA: 0s - loss: 0.7891
25/25 [==============================] - 0s 1ms/step - loss: 0.7756
## Epoch 15/20
##
1/25 [>.............................] - ETA: 0s - loss: 0.7833
25/25 [==============================] - 0s 1ms/step - loss: 0.7672
## Epoch 16/20
##
1/25 [>.............................] - ETA: 0s - loss: 0.7345
25/25 [==============================] - 0s 1ms/step - loss: 0.7589
## Epoch 17/20
##
1/25 [>.............................] - ETA: 0s - loss: 0.8378
25/25 [==============================] - 0s 1ms/step - loss: 0.7509
## Epoch 18/20
##
1/25 [>.............................] - ETA: 0s - loss: 0.7634
25/25 [==============================] - 0s 1ms/step - loss: 0.7430
## Epoch 19/20
##
1/25 [>.............................] - ETA: 0s - loss: 0.7056
25/25 [==============================] - 0s 1ms/step - loss: 0.7351
## Epoch 20/20
##
1/25 [>.............................] - ETA: 0s - loss: 0.6651
25/25 [==============================] - 0s 1ms/step - loss: 0.7280
logreg_cls_fit ## parsnip model object
##
## Model: "sequential_1"
## __________________________________________________________________________
## Layer (type) Output Shape Param #
## ==========================================================================
## dense_2 (Dense) (None, 1) 3
## dense_3 (Dense) (None, 2) 4
## ==========================================================================
## Total params: 7
## Trainable params: 7
## Non-trainable params: 0
## __________________________________________________________________________The holdout data can be predicted for both hard class predictions and probabilities. We’ll bind these together into one tibble:
## # A tibble: 10 × 3
## .pred_class .pred_Class1 .pred_Class2
## <fct> <dbl> <dbl>
## 1 Class1 0.511 0.489
## 2 Class1 0.840 0.160
## 3 Class1 0.522 0.478
## 4 Class1 0.836 0.164
## 5 Class1 0.645 0.355
## 6 Class1 0.542 0.458
## 7 Class1 0.742 0.258
## 8 Class1 0.527 0.473
## 9 Class1 0.990 0.00992
## 10 Class1 0.501 0.499
With the "LiblineaR" engine
Classification Example (LiblineaR)
The example data has two predictors and an outcome with two classes. Both predictors are in the same units.
library(tidymodels)
tidymodels_prefer()
data(two_class_dat)
data_train <- two_class_dat[-(1:10), ]
data_test <- two_class_dat[ 1:10 , ]We can define the model with specific parameters:
logreg_cls_spec <-
logistic_reg(penalty = 0.1) %>%
set_engine("LiblineaR")
logreg_cls_spec ## Logistic Regression Model Specification (classification)
##
## Main Arguments:
## penalty = 0.1
##
## Computational engine: LiblineaRNow we create the model fit object:
## parsnip model object
##
## $TypeDetail
## [1] "L2-regularized logistic regression primal (L2R_LR)"
##
## $Type
## [1] 0
##
## $W
## A B Bias
## [1,] 1.219818 -3.759034 3.674861
##
## $Bias
## [1] 1
##
## $ClassNames
## [1] Class1 Class2
## Levels: Class1 Class2
##
## $NbClass
## [1] 2
##
## attr(,"class")
## [1] "LiblineaR"The holdout data can be predicted for both hard class predictions and probabilities. We’ll bind these together into one tibble:
## # A tibble: 10 × 3
## .pred_class .pred_Class1 .pred_Class2
## <fct> <dbl> <dbl>
## 1 Class1 0.517 0.483
## 2 Class1 0.904 0.0964
## 3 Class1 0.645 0.355
## 4 Class1 0.604 0.396
## 5 Class2 0.442 0.558
## 6 Class2 0.210 0.790
## 7 Class1 0.702 0.298
## 8 Class1 0.565 0.435
## 9 Class1 0.993 0.00667
## 10 Class2 0.112 0.888
With the "stan" engine
Classification Example (stan)
The example data has two predictors and an outcome with two classes. Both predictors are in the same units.
library(tidymodels)
tidymodels_prefer()
data(two_class_dat)
data_train <- two_class_dat[-(1:10), ]
data_test <- two_class_dat[ 1:10 , ]We can define the model with specific parameters:
logreg_cls_spec <-
logistic_reg() %>%
set_engine("stan")
logreg_cls_spec ## Logistic Regression Model Specification (classification)
##
## Computational engine: stanNow we create the model fit object:
## parsnip model object
##
## stan_glm
## family: binomial [logit]
## formula: Class ~ .
## observations: 781
## predictors: 3
## ------
## Median MAD_SD
## (Intercept) -3.8 0.3
## A -1.3 0.2
## B 3.9 0.3
##
## ------
## * For help interpreting the printed output see ?print.stanreg
## * For info on the priors used see ?prior_summary.stanregThe holdout data can be predicted for both hard class predictions and probabilities. We’ll bind these together into one tibble:
## # A tibble: 10 × 3
## .pred_class .pred_Class1 .pred_Class2
## <fct> <dbl> <dbl>
## 1 Class1 0.518 0.482
## 2 Class1 0.909 0.0909
## 3 Class1 0.650 0.350
## 4 Class1 0.609 0.391
## 5 Class2 0.443 0.557
## 6 Class2 0.206 0.794
## 7 Class1 0.708 0.292
## 8 Class1 0.568 0.432
## 9 Class1 0.994 0.00580
## 10 Class2 0.108 0.892
mars() models
With the "earth" engine
Regression Example (earth)
We’ll model the ridership on the Chicago elevated trains as a function of the 14 day lagged ridership at two stations. The two predictors are in the same units (rides per day/1000) and do not need to be normalized. All but the last week of data are used for training. The last week will be predicted after the model is fit.
library(tidymodels)
tidymodels_prefer()
data(Chicago)
n <- nrow(Chicago)
Chicago <- Chicago %>% select(ridership, Clark_Lake, Quincy_Wells)
Chicago_train <- Chicago[1:(n - 7), ]
Chicago_test <- Chicago[(n - 6):n, ]We can define the model with specific parameters:
mars_reg_spec <-
mars(prod_degree = 1, prune_method = "backward") %>%
# This model can be used for classification or regression, so set mode
set_mode("regression") %>%
set_engine("earth")
mars_reg_spec ## MARS Model Specification (regression)
##
## Main Arguments:
## prod_degree = 1
## prune_method = backward
##
## Computational engine: earthNow we create the model fit object:
##
## Attaching package: 'plotrix' ## The following object is masked from 'package:scales':
##
## rescale
mars_reg_fit ## parsnip model object
##
## Selected 5 of 6 terms, and 2 of 2 predictors
## Termination condition: RSq changed by less than 0.001 at 6 terms
## Importance: Clark_Lake, Quincy_Wells
## Number of terms at each degree of interaction: 1 4 (additive model)
## GCV 9.085818 RSS 51543.98 GRSq 0.7889881 RSq 0.789581The holdout data can be predicted:
predict(mars_reg_fit, Chicago_test) ## # A tibble: 7 × 1
## .pred
## <dbl>
## 1 20.4
## 2 20.7
## 3 21.0
## 4 20.7
## 5 19.0
## 6 7.99
## 7 6.68
Classification Example (earth)
The example data has two predictors and an outcome with two classes. Both predictors are in the same units.
library(tidymodels)
tidymodels_prefer()
data(two_class_dat)
data_train <- two_class_dat[-(1:10), ]
data_test <- two_class_dat[ 1:10 , ]We can define the model with specific parameters:
mars_cls_spec <-
mars(prod_degree = 1, prune_method = "backward") %>%
# This model can be used for classification or regression, so set mode
set_mode("classification") %>%
set_engine("earth")
mars_cls_spec ## MARS Model Specification (classification)
##
## Main Arguments:
## prod_degree = 1
## prune_method = backward
##
## Computational engine: earthNow we create the model fit object:
## parsnip model object
##
## GLM (family binomial, link logit):
## nulldev df dev df devratio AIC iters converged
## 1073.43 780 632.723 775 0.411 644.7 5 1
##
## Earth selected 6 of 13 terms, and 2 of 2 predictors
## Termination condition: Reached nk 21
## Importance: B, A
## Number of terms at each degree of interaction: 1 5 (additive model)
## Earth GCV 0.1334948 RSS 101.3432 GRSq 0.461003 RSq 0.4747349The holdout data can be predicted for both hard class predictions and probabilities. We’ll bind these together into one tibble:
## # A tibble: 10 × 3
## .pred_class .pred_Class1 .pred_Class2
## <fct> <dbl> <dbl>
## 1 Class2 0.332 0.668
## 2 Class1 0.845 0.155
## 3 Class1 0.585 0.415
## 4 Class1 0.690 0.310
## 5 Class2 0.483 0.517
## 6 Class2 0.318 0.682
## 7 Class1 0.661 0.339
## 8 Class2 0.398 0.602
## 9 Class1 0.990 0.00972
## 10 Class2 0.0625 0.938
mlp() models
With the "nnet" engine
Regression Example (nnet)
We’ll model the ridership on the Chicago elevated trains as a function of the 14 day lagged ridership at two stations. The two predictors are in the same units (rides per day/1000) and do not need to be normalized. All but the last week of data are used for training. The last week will be predicted after the model is fit.
library(tidymodels)
tidymodels_prefer()
data(Chicago)
n <- nrow(Chicago)
Chicago <- Chicago %>% select(ridership, Clark_Lake, Quincy_Wells)
Chicago_train <- Chicago[1:(n - 7), ]
Chicago_test <- Chicago[(n - 6):n, ]We can define the model with specific parameters:
mlp_reg_spec <-
mlp(penalty = 0, epochs = 100) %>%
# This model can be used for classification or regression, so set mode
set_mode("regression") %>%
set_engine("nnet")
mlp_reg_spec ## Single Layer Neural Network Model Specification (regression)
##
## Main Arguments:
## penalty = 0
## epochs = 100
##
## Computational engine: nnetNow we create the model fit object:
## parsnip model object
##
## a 2-5-1 network with 21 weights
## inputs: Clark_Lake Quincy_Wells
## output(s): ridership
## options were - linear output unitsThe holdout data can be predicted:
predict(mlp_reg_fit, Chicago_test) ## # A tibble: 7 × 1
## .pred
## <dbl>
## 1 20.5
## 2 20.8
## 3 21.1
## 4 20.8
## 5 18.8
## 6 8.09
## 7 6.22
Classification Example (nnet)
The example data has two predictors and an outcome with two classes. Both predictors are in the same units.
library(tidymodels)
tidymodels_prefer()
data(two_class_dat)
data_train <- two_class_dat[-(1:10), ]
data_test <- two_class_dat[ 1:10 , ]We can define the model with specific parameters:
mlp_cls_spec <-
mlp(penalty = 0, epochs = 100) %>%
# This model can be used for classification or regression, so set mode
set_mode("classification") %>%
set_engine("nnet")
mlp_cls_spec ## Single Layer Neural Network Model Specification (classification)
##
## Main Arguments:
## penalty = 0
## epochs = 100
##
## Computational engine: nnetNow we create the model fit object:
## parsnip model object
##
## a 2-5-1 network with 21 weights
## inputs: A B
## output(s): Class
## options were - entropy fittingThe holdout data can be predicted for both hard class predictions and probabilities. We’ll bind these together into one tibble:
## # A tibble: 10 × 3
## .pred_class .pred_Class1 .pred_Class2
## <fct> <dbl> <dbl>
## 1 Class2 0.364 0.636
## 2 Class1 0.691 0.309
## 3 Class1 0.577 0.423
## 4 Class1 0.686 0.314
## 5 Class2 0.466 0.534
## 6 Class2 0.339 0.661
## 7 Class1 0.670 0.330
## 8 Class2 0.384 0.616
## 9 Class1 0.692 0.308
## 10 Class2 0.330 0.670
With the "keras" engine
Regression Example (keras)
We’ll model the ridership on the Chicago elevated trains as a function of the 14 day lagged ridership at two stations. The two predictors are in the same units (rides per day/1000) and do not need to be normalized. All but the last week of data are used for training. The last week will be predicted after the model is fit.
library(tidymodels)
tidymodels_prefer()
data(Chicago)
n <- nrow(Chicago)
Chicago <- Chicago %>% select(ridership, Clark_Lake, Quincy_Wells)
Chicago_train <- Chicago[1:(n - 7), ]
Chicago_test <- Chicago[(n - 6):n, ]We can define the model with specific parameters:
mlp_reg_spec <-
mlp(penalty = 0, epochs = 20) %>%
# This model can be used for classification or regression, so set mode
set_mode("regression") %>%
set_engine("keras")
mlp_reg_spec ## Single Layer Neural Network Model Specification (regression)
##
## Main Arguments:
## penalty = 0
## epochs = 20
##
## Computational engine: kerasNow we create the model fit object:
## Epoch 1/20
##
1/178 [..............................] - ETA: 45s - loss: 218.1396
36/178 [=====>........................] - ETA: 0s - loss: 217.9224
76/178 [===========>..................] - ETA: 0s - loss: 219.6726
120/178 [===================>..........] - ETA: 0s - loss: 214.0638
165/178 [==========================>...] - ETA: 0s - loss: 210.8104
178/178 [==============================] - 0s 1ms/step - loss: 209.8418
## Epoch 2/20
##
1/178 [..............................] - ETA: 0s - loss: 219.1475
48/178 [=======>......................] - ETA: 0s - loss: 196.8954
92/178 [==============>...............] - ETA: 0s - loss: 192.8499
139/178 [======================>.......] - ETA: 0s - loss: 191.4188
178/178 [==============================] - 0s 1ms/step - loss: 190.1941
## Epoch 3/20
##
1/178 [..............................] - ETA: 0s - loss: 200.0297
47/178 [======>.......................] - ETA: 0s - loss: 189.9677
88/178 [=============>................] - ETA: 0s - loss: 183.6814
128/178 [====================>.........] - ETA: 0s - loss: 182.9421
167/178 [===========================>..] - ETA: 0s - loss: 180.9367
178/178 [==============================] - 0s 1ms/step - loss: 180.2685
## Epoch 4/20
##
1/178 [..............................] - ETA: 0s - loss: 200.0738
46/178 [======>.......................] - ETA: 0s - loss: 174.6646
88/178 [=============>................] - ETA: 0s - loss: 173.6880
134/178 [=====================>........] - ETA: 0s - loss: 172.7107
178/178 [==============================] - ETA: 0s - loss: 171.6647
178/178 [==============================] - 0s 1ms/step - loss: 171.6647
## Epoch 5/20
##
1/178 [..............................] - ETA: 0s - loss: 161.5033
43/178 [======>.......................] - ETA: 0s - loss: 162.4931
82/178 [============>.................] - ETA: 0s - loss: 163.3637
122/178 [===================>..........] - ETA: 0s - loss: 164.9662
163/178 [==========================>...] - ETA: 0s - loss: 164.6949
178/178 [==============================] - 0s 1ms/step - loss: 163.6842
## Epoch 6/20
##
1/178 [..............................] - ETA: 0s - loss: 137.8232
39/178 [=====>........................] - ETA: 0s - loss: 164.1154
82/178 [============>.................] - ETA: 0s - loss: 159.5930
126/178 [====================>.........] - ETA: 0s - loss: 158.3841
168/178 [===========================>..] - ETA: 0s - loss: 156.9668
178/178 [==============================] - 0s 1ms/step - loss: 156.1795
## Epoch 7/20
##
1/178 [..............................] - ETA: 0s - loss: 170.7925
46/178 [======>.......................] - ETA: 0s - loss: 153.6719
89/178 [==============>...............] - ETA: 0s - loss: 151.4373
134/178 [=====================>........] - ETA: 0s - loss: 151.1634
178/178 [==============================] - ETA: 0s - loss: 149.0736
178/178 [==============================] - 0s 1ms/step - loss: 149.0736
## Epoch 8/20
##
1/178 [..............................] - ETA: 0s - loss: 121.0130
47/178 [======>.......................] - ETA: 0s - loss: 143.8844
93/178 [==============>...............] - ETA: 0s - loss: 143.3577
140/178 [======================>.......] - ETA: 0s - loss: 143.0316
178/178 [==============================] - 0s 1ms/step - loss: 142.3210
## Epoch 9/20
##
1/178 [..............................] - ETA: 0s - loss: 148.3589
48/178 [=======>......................] - ETA: 0s - loss: 140.2560
93/178 [==============>...............] - ETA: 0s - loss: 136.4347
137/178 [======================>.......] - ETA: 0s - loss: 135.4539
178/178 [==============================] - 0s 1ms/step - loss: 135.8929
## Epoch 10/20
##
1/178 [..............................] - ETA: 0s - loss: 130.9968
45/178 [======>.......................] - ETA: 0s - loss: 126.2507
87/178 [=============>................] - ETA: 0s - loss: 128.6448
127/178 [====================>.........] - ETA: 0s - loss: 129.4828
171/178 [===========================>..] - ETA: 0s - loss: 129.3106
178/178 [==============================] - 0s 1ms/step - loss: 129.7623
## Epoch 11/20
##
1/178 [..............................] - ETA: 0s - loss: 151.0847
47/178 [======>.......................] - ETA: 0s - loss: 126.5225
94/178 [==============>...............] - ETA: 0s - loss: 126.0077
140/178 [======================>.......] - ETA: 0s - loss: 125.1894
178/178 [==============================] - 0s 1ms/step - loss: 123.9262
## Epoch 12/20
##
1/178 [..............................] - ETA: 0s - loss: 131.4167
48/178 [=======>......................] - ETA: 0s - loss: 123.1428
92/178 [==============>...............] - ETA: 0s - loss: 121.0999
137/178 [======================>.......] - ETA: 0s - loss: 119.3862
178/178 [==============================] - 0s 1ms/step - loss: 118.3627
## Epoch 13/20
##
1/178 [..............................] - ETA: 0s - loss: 128.0753
46/178 [======>.......................] - ETA: 0s - loss: 117.7484
92/178 [==============>...............] - ETA: 0s - loss: 116.1777
139/178 [======================>.......] - ETA: 0s - loss: 114.1367
178/178 [==============================] - 0s 1ms/step - loss: 113.0520
## Epoch 14/20
##
1/178 [..............................] - ETA: 0s - loss: 122.5647
47/178 [======>.......................] - ETA: 0s - loss: 109.9250
91/178 [==============>...............] - ETA: 0s - loss: 109.0551
136/178 [=====================>........] - ETA: 0s - loss: 108.1469
178/178 [==============================] - 0s 1ms/step - loss: 107.9693
## Epoch 15/20
##
1/178 [..............................] - ETA: 0s - loss: 112.9315
47/178 [======>.......................] - ETA: 0s - loss: 106.3803
93/178 [==============>...............] - ETA: 0s - loss: 103.5317
136/178 [=====================>........] - ETA: 0s - loss: 103.7816
175/178 [============================>.] - ETA: 0s - loss: 103.0560
178/178 [==============================] - 0s 1ms/step - loss: 103.0912
## Epoch 16/20
##
1/178 [..............................] - ETA: 0s - loss: 87.1802
46/178 [======>.......................] - ETA: 0s - loss: 98.8160
93/178 [==============>...............] - ETA: 0s - loss: 99.6888
141/178 [======================>.......] - ETA: 0s - loss: 98.3634
178/178 [==============================] - 0s 1ms/step - loss: 98.3785
## Epoch 17/20
##
1/178 [..............................] - ETA: 0s - loss: 91.4900
46/178 [======>.......................] - ETA: 0s - loss: 96.3452
91/178 [==============>...............] - ETA: 0s - loss: 95.2448
135/178 [=====================>........] - ETA: 0s - loss: 95.1118
178/178 [==============================] - 0s 1ms/step - loss: 93.7845
## Epoch 18/20
##
1/178 [..............................] - ETA: 0s - loss: 100.7840
48/178 [=======>......................] - ETA: 0s - loss: 90.8389
95/178 [===============>..............] - ETA: 0s - loss: 91.3020
142/178 [======================>.......] - ETA: 0s - loss: 90.1029
178/178 [==============================] - 0s 1ms/step - loss: 89.2772
## Epoch 19/20
##
1/178 [..............................] - ETA: 0s - loss: 93.1992
46/178 [======>.......................] - ETA: 0s - loss: 86.9986
91/178 [==============>...............] - ETA: 0s - loss: 86.3098
138/178 [======================>.......] - ETA: 0s - loss: 85.4697
178/178 [==============================] - 0s 1ms/step - loss: 84.9408
## Epoch 20/20
##
1/178 [..............................] - ETA: 0s - loss: 68.3395
46/178 [======>.......................] - ETA: 0s - loss: 83.8331
88/178 [=============>................] - ETA: 0s - loss: 81.9316
132/178 [=====================>........] - ETA: 0s - loss: 81.0105
172/178 [===========================>..] - ETA: 0s - loss: 80.5621
178/178 [==============================] - 0s 1ms/step - loss: 80.7449
mlp_reg_fit ## parsnip model object
##
## Model: "sequential_2"
## __________________________________________________________________________
## Layer (type) Output Shape Param #
## ==========================================================================
## dense_4 (Dense) (None, 5) 15
## dense_5 (Dense) (None, 1) 6
## ==========================================================================
## Total params: 21
## Trainable params: 21
## Non-trainable params: 0
## __________________________________________________________________________The holdout data can be predicted:
predict(mlp_reg_fit, Chicago_test) ## # A tibble: 7 × 1
## .pred
## <dbl>
## 1 7.50
## 2 7.50
## 3 7.50
## 4 7.50
## 5 7.50
## 6 6.86
## 7 6.69
Classification Example (keras)
The example data has two predictors and an outcome with two classes. Both predictors are in the same units.
library(tidymodels)
tidymodels_prefer()
data(two_class_dat)
data_train <- two_class_dat[-(1:10), ]
data_test <- two_class_dat[ 1:10 , ]We can define the model with specific parameters:
mlp_cls_spec <-
mlp(penalty = 0, epochs = 20) %>%
# This model can be used for classification or regression, so set mode
set_mode("classification") %>%
set_engine("keras")
mlp_cls_spec ## Single Layer Neural Network Model Specification (classification)
##
## Main Arguments:
## penalty = 0
## epochs = 20
##
## Computational engine: kerasNow we create the model fit object:
## Epoch 1/20
##
1/25 [>.............................] - ETA: 7s - loss: 0.7137
25/25 [==============================] - 0s 1ms/step - loss: 0.6988
## Epoch 2/20
##
1/25 [>.............................] - ETA: 0s - loss: 0.6919
25/25 [==============================] - 0s 1ms/step - loss: 0.6926
## Epoch 3/20
##
1/25 [>.............................] - ETA: 0s - loss: 0.6904
25/25 [==============================] - 0s 1ms/step - loss: 0.6871
## Epoch 4/20
##
1/25 [>.............................] - ETA: 0s - loss: 0.6812
25/25 [==============================] - 0s 1ms/step - loss: 0.6820
## Epoch 5/20
##
1/25 [>.............................] - ETA: 0s - loss: 0.6841
25/25 [==============================] - 0s 1ms/step - loss: 0.6771
## Epoch 6/20
##
1/25 [>.............................] - ETA: 0s - loss: 0.6765
25/25 [==============================] - 0s 1ms/step - loss: 0.6725
## Epoch 7/20
##
1/25 [>.............................] - ETA: 0s - loss: 0.6646
25/25 [==============================] - 0s 1ms/step - loss: 0.6680
## Epoch 8/20
##
1/25 [>.............................] - ETA: 0s - loss: 0.6671
25/25 [==============================] - 0s 2ms/step - loss: 0.6637
## Epoch 9/20
##
1/25 [>.............................] - ETA: 0s - loss: 0.6690
25/25 [==============================] - 0s 1ms/step - loss: 0.6595
## Epoch 10/20
##
1/25 [>.............................] - ETA: 0s - loss: 0.6643
25/25 [==============================] - 0s 1ms/step - loss: 0.6553
## Epoch 11/20
##
1/25 [>.............................] - ETA: 0s - loss: 0.6420
25/25 [==============================] - 0s 1ms/step - loss: 0.6513
## Epoch 12/20
##
1/25 [>.............................] - ETA: 0s - loss: 0.6551
25/25 [==============================] - 0s 1ms/step - loss: 0.6472
## Epoch 13/20
##
1/25 [>.............................] - ETA: 0s - loss: 0.6461
25/25 [==============================] - 0s 2ms/step - loss: 0.6432
## Epoch 14/20
##
1/25 [>.............................] - ETA: 0s - loss: 0.6473
25/25 [==============================] - 0s 1ms/step - loss: 0.6390
## Epoch 15/20
##
1/25 [>.............................] - ETA: 0s - loss: 0.6313
25/25 [==============================] - 0s 1ms/step - loss: 0.6350
## Epoch 16/20
##
1/25 [>.............................] - ETA: 0s - loss: 0.6171
25/25 [==============================] - 0s 1ms/step - loss: 0.6308
## Epoch 17/20
##
1/25 [>.............................] - ETA: 0s - loss: 0.6232
25/25 [==============================] - 0s 1ms/step - loss: 0.6267
## Epoch 18/20
##
1/25 [>.............................] - ETA: 0s - loss: 0.6366
25/25 [==============================] - 0s 1ms/step - loss: 0.6223
## Epoch 19/20
##
1/25 [>.............................] - ETA: 0s - loss: 0.6472
25/25 [==============================] - 0s 1ms/step - loss: 0.6180
## Epoch 20/20
##
1/25 [>.............................] - ETA: 0s - loss: 0.6044
25/25 [==============================] - 0s 1ms/step - loss: 0.6136
mlp_cls_fit ## parsnip model object
##
## Model: "sequential_3"
## __________________________________________________________________________
## Layer (type) Output Shape Param #
## ==========================================================================
## dense_6 (Dense) (None, 5) 15
## dense_7 (Dense) (None, 2) 12
## ==========================================================================
## Total params: 27
## Trainable params: 27
## Non-trainable params: 0
## __________________________________________________________________________The holdout data can be predicted for both hard class predictions and probabilities. We’ll bind these together into one tibble:
## # A tibble: 10 × 3
## .pred_class .pred_Class1 .pred_Class2
## <fct> <dbl> <dbl>
## 1 Class1 0.537 0.463
## 2 Class1 0.661 0.339
## 3 Class1 0.594 0.406
## 4 Class2 0.478 0.522
## 5 Class2 0.475 0.525
## 6 Class2 0.412 0.588
## 7 Class1 0.552 0.448
## 8 Class1 0.554 0.446
## 9 Class1 0.742 0.258
## 10 Class2 0.375 0.625
multinom_reg() models
With the "glmnet" engine
Classification Example (glmnet)
We’ll predict the island where the penguins were observed with two variables in the same unit (mm): bill length and bill depth.
library(tidymodels)
tidymodels_prefer()
data(penguins)
penguins <- penguins %>% select(island, starts_with("bill_"))
penguins_train <- penguins[-c(21, 153, 31, 277, 1), ]
penguins_test <- penguins[ c(21, 153, 31, 277, 1), ]We can define the model with specific parameters:
mr_cls_spec <-
multinom_reg(penalty = 0.1) %>%
set_engine("glmnet")
mr_cls_spec## Multinomial Regression Model Specification (classification)
##
## Main Arguments:
## penalty = 0.1
##
## Computational engine: glmnetNow we create the model fit object:
## parsnip model object
##
##
## Call: glmnet::glmnet(x = maybe_matrix(x), y = y, family = "multinomial")
##
## Df %Dev Lambda
## 1 0 0.00 0.31730
## 2 1 3.43 0.28910
## 3 1 6.30 0.26340
## 4 1 8.74 0.24000
## 5 1 10.83 0.21870
## 6 1 12.62 0.19930
## 7 1 14.17 0.18160
## 8 1 15.51 0.16540
## 9 1 16.67 0.15070
## 10 1 17.68 0.13740
## 11 1 18.56 0.12520
## 12 2 19.93 0.11400
## 13 2 21.31 0.10390
## 14 2 22.50 0.09467
## 15 2 23.52 0.08626
## 16 2 24.40 0.07860
## 17 2 25.16 0.07162
## 18 2 25.81 0.06526
## 19 2 26.37 0.05946
## 20 2 26.86 0.05418
## 21 2 27.27 0.04936
## 22 2 27.63 0.04498
## 23 2 27.94 0.04098
## 24 2 28.21 0.03734
## 25 2 28.44 0.03402
## 26 2 28.63 0.03100
## 27 2 28.80 0.02825
## 28 2 28.94 0.02574
## 29 2 29.06 0.02345
## 30 2 29.17 0.02137
## 31 2 29.26 0.01947
## 32 2 29.33 0.01774
## 33 2 29.39 0.01616
## 34 2 29.45 0.01473
## 35 2 29.49 0.01342
## 36 2 29.53 0.01223
## 37 2 29.56 0.01114
## 38 2 29.59 0.01015
## 39 2 29.61 0.00925
## 40 2 29.63 0.00843
## 41 2 29.65 0.00768
## 42 2 29.67 0.00700
## 43 2 29.68 0.00638
## 44 2 29.69 0.00581
## 45 2 29.70 0.00529
## 46 2 29.71 0.00482
## 47 2 29.71 0.00439
## 48 2 29.72 0.00400
## 49 2 29.72 0.00365
## 50 2 29.73 0.00332
## 51 2 29.73 0.00303
## 52 2 29.74 0.00276
## 53 2 29.74 0.00251
## 54 2 29.74 0.00229
## 55 2 29.75 0.00209
## 56 2 29.75 0.00190
## 57 2 29.75 0.00173
## 58 2 29.75 0.00158
## 59 2 29.75 0.00144
## 60 2 29.75 0.00131The holdout data can be predicted for both hard class predictions and probabilities. We’ll bind these together into one tibble:
## # A tibble: 5 × 4
## .pred_class .pred_Biscoe .pred_Dream .pred_Torgersen
## <fct> <dbl> <dbl> <dbl>
## 1 Dream 0.339 0.448 0.214
## 2 Biscoe 0.879 0.0882 0.0331
## 3 Biscoe 0.539 0.317 0.144
## 4 Dream 0.403 0.435 0.162
## 5 Dream 0.297 0.481 0.221
With the "keras" engine
Classification Example (keras)
We’ll predict the island where the penguins were observed with two variables in the same unit (mm): bill length and bill depth.
library(tidymodels)
tidymodels_prefer()
data(penguins)
penguins <- penguins %>% select(island, starts_with("bill_"))
penguins_train <- penguins[-c(21, 153, 31, 277, 1), ]
penguins_test <- penguins[ c(21, 153, 31, 277, 1), ]We can define the model with specific parameters:
mr_cls_spec <-
multinom_reg(penalty = 0.1) %>%
set_engine("keras")
mr_cls_spec## Multinomial Regression Model Specification (classification)
##
## Main Arguments:
## penalty = 0.1
##
## Computational engine: kerasNow we create the model fit object:
## Epoch 1/20
##
1/11 [=>............................] - ETA: 3s - loss: 3.5672
11/11 [==============================] - 0s 2ms/step - loss: 4.0365
## Epoch 2/20
##
1/11 [=>............................] - ETA: 0s - loss: 5.2078
11/11 [==============================] - 0s 1ms/step - loss: 3.7960
## Epoch 3/20
##
1/11 [=>............................] - ETA: 0s - loss: 3.1886
11/11 [==============================] - 0s 1ms/step - loss: 3.5574
## Epoch 4/20
##
1/11 [=>............................] - ETA: 0s - loss: 3.9179
11/11 [==============================] - 0s 1ms/step - loss: 3.3445
## Epoch 5/20
##
1/11 [=>............................] - ETA: 0s - loss: 2.6709
11/11 [==============================] - 0s 1ms/step - loss: 3.1317
## Epoch 6/20
##
1/11 [=>............................] - ETA: 0s - loss: 3.1781
11/11 [==============================] - 0s 1ms/step - loss: 2.9425
## Epoch 7/20
##
1/11 [=>............................] - ETA: 0s - loss: 2.4844
11/11 [==============================] - 0s 1ms/step - loss: 2.7655
## Epoch 8/20
##
1/11 [=>............................] - ETA: 0s - loss: 2.8394
11/11 [==============================] - 0s 1ms/step - loss: 2.6016
## Epoch 9/20
##
1/11 [=>............................] - ETA: 0s - loss: 1.9991
11/11 [==============================] - 0s 1ms/step - loss: 2.4583
## Epoch 10/20
##
1/11 [=>............................] - ETA: 0s - loss: 2.7867
11/11 [==============================] - 0s 1ms/step - loss: 2.3276
## Epoch 11/20
##
1/11 [=>............................] - ETA: 0s - loss: 2.1485
11/11 [==============================] - 0s 1ms/step - loss: 2.2132
## Epoch 12/20
##
1/11 [=>............................] - ETA: 0s - loss: 1.8962
11/11 [==============================] - 0s 2ms/step - loss: 2.1129
## Epoch 13/20
##
1/11 [=>............................] - ETA: 0s - loss: 2.3114
11/11 [==============================] - 0s 2ms/step - loss: 2.0282
## Epoch 14/20
##
1/11 [=>............................] - ETA: 0s - loss: 1.9859
11/11 [==============================] - 0s 1ms/step - loss: 1.9563
## Epoch 15/20
##
1/11 [=>............................] - ETA: 0s - loss: 1.9355
11/11 [==============================] - 0s 1ms/step - loss: 1.8918
## Epoch 16/20
##
1/11 [=>............................] - ETA: 0s - loss: 1.5023
11/11 [==============================] - 0s 1ms/step - loss: 1.8386
## Epoch 17/20
##
1/11 [=>............................] - ETA: 0s - loss: 2.1241
11/11 [==============================] - 0s 1ms/step - loss: 1.7935
## Epoch 18/20
##
1/11 [=>............................] - ETA: 0s - loss: 1.0253
11/11 [==============================] - 0s 1ms/step - loss: 1.7501
## Epoch 19/20
##
1/11 [=>............................] - ETA: 0s - loss: 1.6444
11/11 [==============================] - 0s 1ms/step - loss: 1.7178
## Epoch 20/20
##
1/11 [=>............................] - ETA: 0s - loss: 1.7162
11/11 [==============================] - 0s 1ms/step - loss: 1.6870
mr_cls_fit## parsnip model object
##
## Model: "sequential_4"
## __________________________________________________________________________
## Layer (type) Output Shape Param #
## ==========================================================================
## dense_8 (Dense) (None, 1) 3
## dense_9 (Dense) (None, 3) 6
## ==========================================================================
## Total params: 9
## Trainable params: 9
## Non-trainable params: 0
## __________________________________________________________________________The holdout data can be predicted for both hard class predictions and probabilities. We’ll bind these together into one tibble:
## # A tibble: 5 × 4
## .pred_class .pred_Biscoe .pred_Dream .pred_Torgersen
## <fct> <dbl> <dbl> <dbl>
## 1 Torgersen 0.286 0.0175 0.697
## 2 Dream 0.000112 1.00 0.00000200
## 3 Dream 0.319 0.474 0.208
## 4 Dream 0.0496 0.939 0.0113
## 5 Torgersen 0.304 0.0235 0.673
With the "nnet" engine
Classification Example (nnet)
We’ll predict the island where the penguins were observed with two variables in the same unit (mm): bill length and bill depth.
library(tidymodels)
tidymodels_prefer()
data(penguins)
penguins <- penguins %>% select(island, starts_with("bill_"))
penguins_train <- penguins[-c(21, 153, 31, 277, 1), ]
penguins_test <- penguins[ c(21, 153, 31, 277, 1), ]We can define the model with specific parameters:
mr_cls_spec <-
multinom_reg(penalty = 0.1) %>%
set_engine("nnet")
mr_cls_spec## Multinomial Regression Model Specification (classification)
##
## Main Arguments:
## penalty = 0.1
##
## Computational engine: nnetNow we create the model fit object:
## parsnip model object
##
## Call:
## nnet::multinom(formula = island ~ ., data = data, decay = ~0.1,
## trace = FALSE)
##
## Coefficients:
## (Intercept) bill_length_mm bill_depth_mm
## Dream -8.243575 -0.0580960 0.6168318
## Torgersen -1.610588 -0.2789588 0.6978480
##
## Residual Deviance: 502.5009
## AIC: 514.5009The holdout data can be predicted for both hard class predictions and probabilities. We’ll bind these together into one tibble:
## # A tibble: 5 × 4
## .pred_class .pred_Biscoe .pred_Dream .pred_Torgersen
## <fct> <dbl> <dbl> <dbl>
## 1 Dream 0.193 0.450 0.357
## 2 Biscoe 0.937 0.0582 0.00487
## 3 Biscoe 0.462 0.364 0.174
## 4 Dream 0.450 0.495 0.0556
## 5 Dream 0.183 0.506 0.311
nearest_neighbor() models
With the "kknn" engine
Regression Example (kknn)
We’ll model the ridership on the Chicago elevated trains as a function of the 14 day lagged ridership at two stations. The two predictors are in the same units (rides per day/1000) and do not need to be normalized. All but the last week of data are used for training. The last week will be predicted after the model is fit.
library(tidymodels)
tidymodels_prefer()
data(Chicago)
n <- nrow(Chicago)
Chicago <- Chicago %>% select(ridership, Clark_Lake, Quincy_Wells)
Chicago_train <- Chicago[1:(n - 7), ]
Chicago_test <- Chicago[(n - 6):n, ]We can define the model with specific parameters:
knn_reg_spec <-
nearest_neighbor(neighbors = 5, weight_func = "triangular") %>%
# This model can be used for classification or regression, so set mode
set_mode("regression") %>%
set_engine("kknn")
knn_reg_spec## K-Nearest Neighbor Model Specification (regression)
##
## Main Arguments:
## neighbors = 5
## weight_func = triangular
##
## Computational engine: kknnNow we create the model fit object:
## parsnip model object
##
##
## Call:
## kknn::train.kknn(formula = ridership ~ ., data = data, ks = min_rows(5, data, 5), kernel = ~"triangular")
##
## Type of response variable: continuous
## minimal mean absolute error: 1.79223
## Minimal mean squared error: 11.21809
## Best kernel: triangular
## Best k: 5The holdout data can be predicted:
predict(knn_reg_fit, Chicago_test)## # A tibble: 7 × 1
## .pred
## <dbl>
## 1 20.5
## 2 21.1
## 3 21.4
## 4 21.8
## 5 19.5
## 6 7.83
## 7 5.54
Classification Example (kknn)
The example data has two predictors and an outcome with two classes. Both predictors are in the same units.
library(tidymodels)
tidymodels_prefer()
data(two_class_dat)
data_train <- two_class_dat[-(1:10), ]
data_test <- two_class_dat[ 1:10 , ]Since there are two classes, we’ll use an odd number of neighbors to avoid ties:
knn_cls_spec <-
nearest_neighbor(neighbors = 11, weight_func = "triangular") %>%
# This model can be used for classification or regression, so set mode
set_mode("classification") %>%
set_engine("kknn")
knn_cls_spec## K-Nearest Neighbor Model Specification (classification)
##
## Main Arguments:
## neighbors = 11
## weight_func = triangular
##
## Computational engine: kknnNow we create the model fit object:
## parsnip model object
##
##
## Call:
## kknn::train.kknn(formula = Class ~ ., data = data, ks = min_rows(11, data, 5), kernel = ~"triangular")
##
## Type of response variable: nominal
## Minimal misclassification: 0.1869398
## Best kernel: triangular
## Best k: 11The holdout data can be predicted for both hard class predictions and probabilities. We’ll bind these together into one tibble:
## # A tibble: 10 × 3
## .pred_class .pred_Class1 .pred_Class2
## <fct> <dbl> <dbl>
## 1 Class2 0.177 0.823
## 2 Class1 0.995 0.00515
## 3 Class1 0.590 0.410
## 4 Class1 0.770 0.230
## 5 Class2 0.333 0.667
## 6 Class2 0.182 0.818
## 7 Class1 0.692 0.308
## 8 Class2 0.400 0.600
## 9 Class1 0.814 0.186
## 10 Class2 0.0273 0.973
rand_forest() models
With the "ranger" engine
Regression Example (ranger)
We’ll model the ridership on the Chicago elevated trains as a function of the 14 day lagged ridership at two stations. The two predictors are in the same units (rides per day/1000) and do not need to be normalized. All but the last week of data are used for training. The last week will be predicted after the model is fit.
library(tidymodels)
tidymodels_prefer()
data(Chicago)
n <- nrow(Chicago)
Chicago <- Chicago %>% select(ridership, Clark_Lake, Quincy_Wells)
Chicago_train <- Chicago[1:(n - 7), ]
Chicago_test <- Chicago[(n - 6):n, ]We can define the model with specific parameters:
rf_reg_spec <-
rand_forest(trees = 200, min_n = 5) %>%
# This model can be used for classification or regression, so set mode
set_mode("regression") %>%
set_engine("ranger")
rf_reg_spec ## Random Forest Model Specification (regression)
##
## Main Arguments:
## trees = 200
## min_n = 5
##
## Computational engine: rangerNow we create the model fit object:
## parsnip model object
##
## Ranger result
##
## Call:
## ranger::ranger(x = maybe_data_frame(x), y = y, num.trees = ~200, min.node.size = min_rows(~5, x), num.threads = 1, verbose = FALSE, seed = sample.int(10^5, 1))
##
## Type: Regression
## Number of trees: 200
## Sample size: 5691
## Number of independent variables: 2
## Mtry: 1
## Target node size: 5
## Variable importance mode: none
## Splitrule: variance
## OOB prediction error (MSE): 9.72953
## R squared (OOB): 0.7739986The holdout data can be predicted:
predict(rf_reg_fit, Chicago_test) ## # A tibble: 7 × 1
## .pred
## <dbl>
## 1 20.4
## 2 21.5
## 3 20.8
## 4 21.6
## 5 19.4
## 6 7.32
## 7 6.03
Classification Example (ranger)
The example data has two predictors and an outcome with two classes. Both predictors are in the same units.
library(tidymodels)
tidymodels_prefer()
data(two_class_dat)
data_train <- two_class_dat[-(1:10), ]
data_test <- two_class_dat[ 1:10 , ]We can define the model with specific parameters:
rf_cls_spec <-
rand_forest(trees = 200, min_n = 5) %>%
# This model can be used for classification or regression, so set mode
set_mode("classification") %>%
set_engine("ranger")
rf_cls_spec ## Random Forest Model Specification (classification)
##
## Main Arguments:
## trees = 200
## min_n = 5
##
## Computational engine: rangerNow we create the model fit object:
## parsnip model object
##
## Ranger result
##
## Call:
## ranger::ranger(x = maybe_data_frame(x), y = y, num.trees = ~200, min.node.size = min_rows(~5, x), num.threads = 1, verbose = FALSE, seed = sample.int(10^5, 1), probability = TRUE)
##
## Type: Probability estimation
## Number of trees: 200
## Sample size: 781
## Number of independent variables: 2
## Mtry: 1
## Target node size: 5
## Variable importance mode: none
## Splitrule: gini
## OOB prediction error (Brier s.): 0.1534794The holdout data can be predicted for both hard class predictions and probabilities. We’ll bind these together into one tibble:
## # A tibble: 10 × 3
## .pred_class .pred_Class1 .pred_Class2
## <fct> <dbl> <dbl>
## 1 Class2 0.274 0.725
## 2 Class1 0.928 0.0716
## 3 Class2 0.497 0.503
## 4 Class1 0.703 0.297
## 5 Class2 0.302 0.698
## 6 Class2 0.151 0.849
## 7 Class1 0.701 0.299
## 8 Class1 0.592 0.409
## 9 Class1 0.752 0.248
## 10 Class2 0.00225 0.998
With the "randomForest" engine
Regression Example (randomForest)
We’ll model the ridership on the Chicago elevated trains as a function of the 14 day lagged ridership at two stations. The two predictors are in the same units (rides per day/1000) and do not need to be normalized. All but the last week of data are used for training. The last week will be predicted after the model is fit.
library(tidymodels)
tidymodels_prefer()
data(Chicago)
n <- nrow(Chicago)
Chicago <- Chicago %>% select(ridership, Clark_Lake, Quincy_Wells)
Chicago_train <- Chicago[1:(n - 7), ]
Chicago_test <- Chicago[(n - 6):n, ]We can define the model with specific parameters:
rf_reg_spec <-
rand_forest(trees = 200, min_n = 5) %>%
# This model can be used for classification or regression, so set mode
set_mode("regression") %>%
set_engine("randomForest")
rf_reg_spec ## Random Forest Model Specification (regression)
##
## Main Arguments:
## trees = 200
## min_n = 5
##
## Computational engine: randomForestNow we create the model fit object:
## parsnip model object
##
##
## Call:
## randomForest(x = maybe_data_frame(x), y = y, ntree = ~200, nodesize = min_rows(~5, x))
## Type of random forest: regression
## Number of trees: 200
## No. of variables tried at each split: 1
##
## Mean of squared residuals: 9.696736
## % Var explained: 77.47The holdout data can be predicted:
predict(rf_reg_fit, Chicago_test) ## # A tibble: 7 × 1
## .pred
## <dbl>
## 1 20.4
## 2 21.6
## 3 20.9
## 4 21.6
## 5 19.3
## 6 7.33
## 7 6.16
Classification Example (randomForest)
The example data has two predictors and an outcome with two classes. Both predictors are in the same units.
library(tidymodels)
tidymodels_prefer()
data(two_class_dat)
data_train <- two_class_dat[-(1:10), ]
data_test <- two_class_dat[ 1:10 , ]We can define the model with specific parameters:
rf_cls_spec <-
rand_forest(trees = 200, min_n = 5) %>%
# This model can be used for classification or regression, so set mode
set_mode("classification") %>%
set_engine("randomForest")
rf_cls_spec ## Random Forest Model Specification (classification)
##
## Main Arguments:
## trees = 200
## min_n = 5
##
## Computational engine: randomForestNow we create the model fit object:
## parsnip model object
##
##
## Call:
## randomForest(x = maybe_data_frame(x), y = y, ntree = ~200, nodesize = min_rows(~5, x))
## Type of random forest: classification
## Number of trees: 200
## No. of variables tried at each split: 1
##
## OOB estimate of error rate: 19.72%
## Confusion matrix:
## Class1 Class2 class.error
## Class1 363 70 0.1616628
## Class2 84 264 0.2413793The holdout data can be predicted for both hard class predictions and probabilities. We’ll bind these together into one tibble:
## # A tibble: 10 × 3
## .pred_class .pred_Class1 .pred_Class2
## <fct> <dbl> <dbl>
## 1 Class2 0.23 0.77
## 2 Class1 0.95 0.05
## 3 Class1 0.59 0.41
## 4 Class1 0.75 0.25
## 5 Class2 0.305 0.695
## 6 Class2 0.105 0.895
## 7 Class1 0.685 0.315
## 8 Class1 0.63 0.37
## 9 Class1 0.79 0.21
## 10 Class2 0.02 0.98
svm_linear() models
With the "LiblineaR" engine
Regression Example (LiblineaR)
We’ll model the ridership on the Chicago elevated trains as a function of the 14 day lagged ridership at two stations. The two predictors are in the same units (rides per day/1000) and do not need to be normalized. All but the last week of data are used for training. The last week will be predicted after the model is fit.
library(tidymodels)
tidymodels_prefer()
data(Chicago)
n <- nrow(Chicago)
Chicago <- Chicago %>% select(ridership, Clark_Lake, Quincy_Wells)
Chicago_train <- Chicago[1:(n - 7), ]
Chicago_test <- Chicago[(n - 6):n, ]We can define the model with specific parameters:
svm_reg_spec <-
svm_linear(cost = 1, margin = 0.1) %>%
# This model can be used for classification or regression, so set mode
set_mode("regression") %>%
set_engine("LiblineaR")
svm_reg_spec ## Linear Support Vector Machine Model Specification (regression)
##
## Main Arguments:
## cost = 1
## margin = 0.1
##
## Computational engine: LiblineaRNow we create the model fit object:
## parsnip model object
##
## $TypeDetail
## [1] "L2-regularized L2-loss support vector regression primal (L2R_L2LOSS_SVR)"
##
## $Type
## [1] 11
##
## $W
## Clark_Lake Quincy_Wells Bias
## [1,] 0.8277352 0.3430336 0.05042585
##
## $Bias
## [1] 1
##
## $NbClass
## [1] 2
##
## attr(,"class")
## [1] "LiblineaR"The holdout data can be predicted:
predict(svm_reg_fit, Chicago_test) ## # A tibble: 7 × 1
## .pred
## <dbl>
## 1 20.6
## 2 20.8
## 3 21.1
## 4 20.8
## 5 18.9
## 6 6.40
## 7 5.90
Classification Example (LiblineaR)
The example data has two predictors and an outcome with two classes. Both predictors are in the same units.
library(tidymodels)
tidymodels_prefer()
data(two_class_dat)
data_train <- two_class_dat[-(1:10), ]
data_test <- two_class_dat[ 1:10 , ]We can define the model with specific parameters:
svm_cls_spec <-
svm_linear(cost = 1) %>%
# This model can be used for classification or regression, so set mode
set_mode("classification") %>%
set_engine("LiblineaR")
svm_cls_spec ## Linear Support Vector Machine Model Specification (classification)
##
## Main Arguments:
## cost = 1
##
## Computational engine: LiblineaRNow we create the model fit object:
## parsnip model object
##
## $TypeDetail
## [1] "L2-regularized L2-loss support vector classification dual (L2R_L2LOSS_SVC_DUAL)"
##
## $Type
## [1] 1
##
## $W
## A B Bias
## [1,] 0.4067922 -1.314783 1.321851
##
## $Bias
## [1] 1
##
## $ClassNames
## [1] Class1 Class2
## Levels: Class1 Class2
##
## $NbClass
## [1] 2
##
## attr(,"class")
## [1] "LiblineaR"The holdout data can be predicted for hard class predictions.
predict(svm_cls_fit, data_test) ## # A tibble: 10 × 1
## .pred_class
## <fct>
## 1 Class1
## 2 Class1
## 3 Class1
## 4 Class1
## 5 Class2
## 6 Class2
## 7 Class1
## 8 Class1
## 9 Class1
## 10 Class2
With the "kernlab" engine
Regression Example (kernlab)
We’ll model the ridership on the Chicago elevated trains as a function of the 14 day lagged ridership at two stations. The two predictors are in the same units (rides per day/1000) and do not need to be normalized. All but the last week of data are used for training. The last week will be predicted after the model is fit.
library(tidymodels)
tidymodels_prefer()
data(Chicago)
n <- nrow(Chicago)
Chicago <- Chicago %>% select(ridership, Clark_Lake, Quincy_Wells)
Chicago_train <- Chicago[1:(n - 7), ]
Chicago_test <- Chicago[(n - 6):n, ]We can define the model with specific parameters:
svm_reg_spec <-
svm_linear(cost = 1, margin = 0.1) %>%
# This model can be used for classification or regression, so set mode
set_mode("regression") %>%
set_engine("kernlab")
svm_reg_spec ## Linear Support Vector Machine Model Specification (regression)
##
## Main Arguments:
## cost = 1
## margin = 0.1
##
## Computational engine: kernlabNow we create the model fit object:
## Setting default kernel parameters
svm_reg_fit ## parsnip model object
##
## Support Vector Machine object of class "ksvm"
##
## SV type: eps-svr (regression)
## parameter : epsilon = 0.1 cost C = 1
##
## Linear (vanilla) kernel function.
##
## Number of Support Vectors : 2283
##
## Objective Function Value : -825.1632
## Training error : 0.226456The holdout data can be predicted:
predict(svm_reg_fit, Chicago_test) ## # A tibble: 7 × 1
## .pred
## <dbl>
## 1 21.0
## 2 21.2
## 3 21.5
## 4 21.2
## 5 19.4
## 6 6.87
## 7 6.41
Classification Example (kernlab)
The example data has two predictors and an outcome with two classes. Both predictors are in the same units.
library(tidymodels)
tidymodels_prefer()
data(two_class_dat)
data_train <- two_class_dat[-(1:10), ]
data_test <- two_class_dat[ 1:10 , ]We can define the model with specific parameters:
svm_cls_spec <-
svm_linear(cost = 1) %>%
# This model can be used for classification or regression, so set mode
set_mode("classification") %>%
set_engine("kernlab")
svm_cls_spec ## Linear Support Vector Machine Model Specification (classification)
##
## Main Arguments:
## cost = 1
##
## Computational engine: kernlabNow we create the model fit object:
## Setting default kernel parameters
svm_cls_fit ## parsnip model object
##
## Support Vector Machine object of class "ksvm"
##
## SV type: C-svc (classification)
## parameter : cost C = 1
##
## Linear (vanilla) kernel function.
##
## Number of Support Vectors : 353
##
## Objective Function Value : -349.425
## Training error : 0.174136
## Probability model included.The holdout data can be predicted for both hard class predictions and probabilities. We’ll bind these together into one tibble:
## # A tibble: 10 × 3
## .pred_class .pred_Class1 .pred_Class2
## <fct> <dbl> <dbl>
## 1 Class1 0.517 0.483
## 2 Class1 0.904 0.0956
## 3 Class1 0.645 0.355
## 4 Class1 0.610 0.390
## 5 Class2 0.445 0.555
## 6 Class2 0.212 0.788
## 7 Class1 0.704 0.296
## 8 Class1 0.565 0.435
## 9 Class1 0.994 0.00646
## 10 Class2 0.114 0.886
svm_poly() models
With the "kernlab" engine
Regression Example (kernlab)
We’ll model the ridership on the Chicago elevated trains as a function of the 14 day lagged ridership at two stations. The two predictors are in the same units (rides per day/1000) and do not need to be normalized. All but the last week of data are used for training. The last week will be predicted after the model is fit.
library(tidymodels)
tidymodels_prefer()
data(Chicago)
n <- nrow(Chicago)
Chicago <- Chicago %>% select(ridership, Clark_Lake, Quincy_Wells)
Chicago_train <- Chicago[1:(n - 7), ]
Chicago_test <- Chicago[(n - 6):n, ]We can define the model with specific parameters:
svm_reg_spec <-
svm_poly(cost = 1, margin = 0.1) %>%
# This model can be used for classification or regression, so set mode
set_mode("regression") %>%
set_engine("kernlab")
svm_reg_spec ## Polynomial Support Vector Machine Model Specification (regression)
##
## Main Arguments:
## cost = 1
## margin = 0.1
##
## Computational engine: kernlabNow we create the model fit object:
## Setting default kernel parameters
svm_reg_fit ## parsnip model object
##
## Support Vector Machine object of class "ksvm"
##
## SV type: eps-svr (regression)
## parameter : epsilon = 0.1 cost C = 1
##
## Polynomial kernel function.
## Hyperparameters : degree = 1 scale = 1 offset = 1
##
## Number of Support Vectors : 2283
##
## Objective Function Value : -825.1628
## Training error : 0.226471The holdout data can be predicted:
predict(svm_reg_fit, Chicago_test) ## # A tibble: 7 × 1
## .pred
## <dbl>
## 1 21.0
## 2 21.2
## 3 21.5
## 4 21.2
## 5 19.4
## 6 6.87
## 7 6.41
Classification Example (kernlab)
The example data has two predictors and an outcome with two classes. Both predictors are in the same units.
library(tidymodels)
tidymodels_prefer()
data(two_class_dat)
data_train <- two_class_dat[-(1:10), ]
data_test <- two_class_dat[ 1:10 , ]We can define the model with specific parameters:
svm_cls_spec <-
svm_poly(cost = 1) %>%
# This model can be used for classification or regression, so set mode
set_mode("classification") %>%
set_engine("kernlab")
svm_cls_spec ## Polynomial Support Vector Machine Model Specification (classification)
##
## Main Arguments:
## cost = 1
##
## Computational engine: kernlabNow we create the model fit object:
## Setting default kernel parameters
svm_cls_fit ## parsnip model object
##
## Support Vector Machine object of class "ksvm"
##
## SV type: C-svc (classification)
## parameter : cost C = 1
##
## Polynomial kernel function.
## Hyperparameters : degree = 1 scale = 1 offset = 1
##
## Number of Support Vectors : 353
##
## Objective Function Value : -349.425
## Training error : 0.174136
## Probability model included.The holdout data can be predicted for both hard class predictions and probabilities. We’ll bind these together into one tibble:
## # A tibble: 10 × 3
## .pred_class .pred_Class1 .pred_Class2
## <fct> <dbl> <dbl>
## 1 Class1 0.517 0.483
## 2 Class1 0.904 0.0956
## 3 Class1 0.645 0.355
## 4 Class1 0.610 0.390
## 5 Class2 0.445 0.555
## 6 Class2 0.212 0.788
## 7 Class1 0.704 0.296
## 8 Class1 0.565 0.435
## 9 Class1 0.994 0.00646
## 10 Class2 0.114 0.886
svm_rbf() models
With the "kernlab" engine
Regression Example (kernlab)
We’ll model the ridership on the Chicago elevated trains as a function of the 14 day lagged ridership at two stations. The two predictors are in the same units (rides per day/1000) and do not need to be normalized. All but the last week of data are used for training. The last week will be predicted after the model is fit.
library(tidymodels)
tidymodels_prefer()
data(Chicago)
n <- nrow(Chicago)
Chicago <- Chicago %>% select(ridership, Clark_Lake, Quincy_Wells)
Chicago_train <- Chicago[1:(n - 7), ]
Chicago_test <- Chicago[(n - 6):n, ]We can define the model with specific parameters:
svm_reg_spec <-
svm_rbf(cost = 1, margin = 0.1) %>%
# This model can be used for classification or regression, so set mode
set_mode("regression") %>%
set_engine("kernlab")
svm_reg_spec## Radial Basis Function Support Vector Machine Model Specification (regression)
##
## Main Arguments:
## cost = 1
## margin = 0.1
##
## Computational engine: kernlabNow we create the model fit object:
## parsnip model object
##
## Support Vector Machine object of class "ksvm"
##
## SV type: eps-svr (regression)
## parameter : epsilon = 0.1 cost C = 1
##
## Gaussian Radial Basis kernel function.
## Hyperparameter : sigma = 10.8262370251485
##
## Number of Support Vectors : 2233
##
## Objective Function Value : -746.584
## Training error : 0.205567The holdout data can be predicted:
predict(svm_reg_fit, Chicago_test)## # A tibble: 7 × 1
## .pred
## <dbl>
## 1 20.7
## 2 21.2
## 3 21.3
## 4 21.1
## 5 19.4
## 6 6.77
## 7 6.13
Classification Example (kernlab)
The example data has two predictors and an outcome with two classes. Both predictors are in the same units.
library(tidymodels)
tidymodels_prefer()
data(two_class_dat)
data_train <- two_class_dat[-(1:10), ]
data_test <- two_class_dat[ 1:10 , ]We can define the model with specific parameters:
svm_cls_spec <-
svm_rbf(cost = 1) %>%
# This model can be used for classification or regression, so set mode
set_mode("classification") %>%
set_engine("kernlab")
svm_cls_spec## Radial Basis Function Support Vector Machine Model Specification (classification)
##
## Main Arguments:
## cost = 1
##
## Computational engine: kernlabNow we create the model fit object:
## parsnip model object
##
## Support Vector Machine object of class "ksvm"
##
## SV type: C-svc (classification)
## parameter : cost C = 1
##
## Gaussian Radial Basis kernel function.
## Hyperparameter : sigma = 1.63216688499952
##
## Number of Support Vectors : 327
##
## Objective Function Value : -294.4344
## Training error : 0.169014
## Probability model included.The holdout data can be predicted for both hard class predictions and probabilities. We’ll bind these together into one tibble:
## # A tibble: 10 × 3
## .pred_class .pred_Class1 .pred_Class2
## <fct> <dbl> <dbl>
## 1 Class2 0.238 0.762
## 2 Class1 0.905 0.0950
## 3 Class1 0.619 0.381
## 4 Class1 0.879 0.121
## 5 Class1 0.641 0.359
## 6 Class2 0.153 0.847
## 7 Class1 0.745 0.255
## 8 Class2 0.313 0.687
## 9 Class1 0.878 0.122
## 10 Class2 0.137 0.863