Apply a model to create different types of predictions.
predict() can be used for all types of models and uses the
"type" argument for more specificity.
Arguments
- object
An object of class
model_fit.- new_data
A rectangular data object, such as a data frame.
- type
A single character value or
NULL. Possible values are"numeric","class","prob","conf_int","pred_int","quantile","time","hazard","survival", or"raw". WhenNULL,predict()will choose an appropriate value based on the model's mode.- opts
A list of optional arguments to the underlying predict function that will be used when
type = "raw". The list should not include options for the model object or the new data being predicted.- ...
Additional
parsnip-related options, depending on the value oftype. Arguments to the underlying model's prediction function cannot be passed here (use theoptsargument instead). Possible arguments are:interval: fortypeequal to"survival"or"quantile", should interval estimates be added, if available? Options are"none"and"confidence".level: fortypeequal to"conf_int","pred_int", or"survival", this is the parameter for the tail area of the intervals (e.g. confidence level for confidence intervals). Default value is0.95.std_error: fortypeequal to"conf_int"or"pred_int", add the standard error of fit or prediction (on the scale of the linear predictors). Default value isFALSE.quantile: fortypeequal toquantile, the quantiles of the distribution. Default is(1:9)/10.eval_time: fortypeequal to"survival"or"hazard", the time points at which the survival probability or hazard is estimated.
Value
With the exception of type = "raw", the result of
predict.model_fit()
is a tibble
has as many rows as there are rows in
new_datahas standardized column names, see below:
For type = "numeric", the tibble has a .pred column for a single
outcome and .pred_Yname columns for a multivariate outcome.
For type = "class", the tibble has a .pred_class column.
For type = "prob", the tibble has .pred_classlevel columns.
For type = "conf_int" and type = "pred_int", the tibble has
.pred_lower and .pred_upper columns with an attribute for
the confidence level. In the case where intervals can be
produces for class probabilities (or other non-scalar outputs),
the columns are named .pred_lower_classlevel and so on.
For type = "quantile", the tibble has a .pred column, which is
a list-column. Each list element contains a tibble with columns
.pred and .quantile (and perhaps other columns).
For type = "time", the tibble has a .pred_time column.
For type = "survival", the tibble has a .pred column, which is
a list-column. Each list element contains a tibble with columns
.eval_time and .pred_survival (and perhaps other columns).
For type = "hazard", the tibble has a .pred column, which is
a list-column. Each list element contains a tibble with columns
.eval_time and .pred_hazard (and perhaps other columns).
Using type = "raw" with predict.model_fit() will return
the unadulterated results of the prediction function.
In the case of Spark-based models, since table columns cannot contain dots, the same convention is used except 1) no dots appear in names and 2) vectors are never returned but type-specific prediction functions.
When the model fit failed and the error was captured, the
predict() function will return the same structure as above but
filled with missing values. This does not currently work for
multivariate models.
Details
For type = NULL, predict() uses
type = "numeric"for regression models,type = "class"for classification, andtype = "time"for censored regression.
Interval predictions
When using type = "conf_int" and type = "pred_int", the options
level and std_error can be used. The latter is a logical for an
extra column of standard error values (if available).
Censored regression predictions
For censored regression, a numeric vector for eval_time is required when
survival or hazard probabilities are requested. The time values are required
to be unique, finite, non-missing, and non-negative. The predict()
functions will adjust the values to fit this specification by removing
offending points (with a warning).
predict.model_fit() does not require the outcome to be present. For
performance metrics on the predicted survival probability, inverse probability
of censoring weights (IPCW) are required (see the tidymodels.org reference
below). Those require the outcome and are thus not returned by predict().
They can be added via augment.model_fit() if new_data contains a column
with the outcome as a Surv object.
Also, when type = "linear_pred", censored regression models will by default
be formatted such that the linear predictor increases with time. This may
have the opposite sign as what the underlying model's predict() method
produces. Set increasing = FALSE to suppress this behavior.
Examples
library(dplyr)
lm_model <-
linear_reg() %>%
set_engine("lm") %>%
fit(mpg ~ ., data = mtcars %>% dplyr::slice(11:32))
pred_cars <-
mtcars %>%
dplyr::slice(1:10) %>%
dplyr::select(-mpg)
predict(lm_model, pred_cars)
#> # A tibble: 10 × 1
#> .pred
#> <dbl>
#> 1 23.4
#> 2 23.3
#> 3 27.6
#> 4 21.5
#> 5 17.6
#> 6 21.6
#> 7 13.9
#> 8 21.7
#> 9 25.6
#> 10 17.1
predict(
lm_model,
pred_cars,
type = "conf_int",
level = 0.90
)
#> # A tibble: 10 × 2
#> .pred_lower .pred_upper
#> <dbl> <dbl>
#> 1 17.9 29.0
#> 2 18.1 28.5
#> 3 24.0 31.3
#> 4 17.5 25.6
#> 5 14.3 20.8
#> 6 17.0 26.2
#> 7 9.65 18.2
#> 8 16.2 27.2
#> 9 14.2 37.0
#> 10 11.5 22.7
predict(
lm_model,
pred_cars,
type = "raw",
opts = list(type = "terms")
)
#> cyl disp hp drat
#> Mazda RX4 -0.001433177 -0.8113275 0.6303467 -0.06120265
#> Mazda RX4 Wag -0.001433177 -0.8113275 0.6303467 -0.06120265
#> Datsun 710 -0.009315653 -1.3336453 0.8557288 -0.05014798
#> Hornet 4 Drive -0.001433177 0.1730406 0.6303467 0.12009386
#> Hornet Sportabout 0.006449298 1.1975870 -0.2314083 0.10461733
#> Valiant -0.001433177 -0.1584303 0.6966356 0.19084372
#> Duster 360 0.006449298 1.1975870 -1.1594522 0.09135173
#> Merc 240D -0.009315653 -0.9449204 1.2667197 -0.01477305
#> Merc 230 -0.009315653 -1.0041833 0.8292133 -0.06562451
#> Merc 280 -0.001433177 -0.7349888 0.4579957 -0.06562451
#> wt qsec vs am gear
#> Mazda RX4 2.4139815 -1.567729 0.2006406 2.88774 0.02512680
#> Mazda RX4 Wag 1.4488706 -0.736286 0.2006406 2.88774 0.02512680
#> Datsun 710 3.5494061 1.624418 -0.3511210 2.88774 0.02512680
#> Hornet 4 Drive 0.1620561 2.856736 -0.3511210 -2.40645 -0.06700481
#> Hornet Sportabout -0.6895124 -0.736286 0.2006406 -2.40645 -0.06700481
#> Valiant -0.7652074 4.014817 -0.3511210 -2.40645 -0.06700481
#> Duster 360 -1.1815297 -2.488255 0.2006406 -2.40645 -0.06700481
#> Merc 240D 0.2566748 3.688179 -0.3511210 -2.40645 0.02512680
#> Merc 230 0.4080647 7.993866 -0.3511210 -2.40645 0.02512680
#> Merc 280 -0.6895124 1.164155 -0.3511210 -2.40645 0.02512680
#> carb
#> Mazda RX4 -0.2497240
#> Mazda RX4 Wag -0.2497240
#> Datsun 710 0.4668753
#> Hornet 4 Drive 0.4668753
#> Hornet Sportabout 0.2280089
#> Valiant 0.4668753
#> Duster 360 -0.2497240
#> Merc 240D 0.2280089
#> Merc 230 0.2280089
#> Merc 280 -0.2497240
#> attr(,"constant")
#> [1] 19.96364