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.
# S3 method for model_fit predict(object, new_data, type = NULL, opts = list(), ...) # S3 method for model_fit predict_raw(object, new_data, opts = list(), ...) predict_raw(object, ...)
| object | An object of class |
|---|---|
| new_data | A rectangular data object, such as a data frame. |
| type | A single character value or |
| opts | A list of optional arguments to the underlying
predict function that will be used when |
| ... | Arguments to the underlying model's prediction
function cannot be passed here (see
|
With the exception of type = "raw", the results of
predict.model_fit() will be a tibble as many rows in the output
as there are rows in new_data and the column names will be
predictable.
For numeric results with a single outcome, the tibble will have
a .pred column and .pred_Yname for multivariate results.
For hard class predictions, the column is named .pred_class
and, when type = "prob", the columns are .pred_classlevel.
type = "conf_int" and type = "pred_int" return tibbles with
columns .pred_lower and .pred_upper 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 will be named .pred_lower_classlevel and so on.
Quantile predictions return a tibble with a column .pred, which is
a list-column. Each list element contains a tibble with columns
.pred and .quantile (and perhaps other columns).
Using type = "raw" with predict.model_fit() will return
the unadulterated results of the prediction function.
For censored regression:
type = "time" produces a column .pred_time.
type = "hazard" results in a column .pred_hazard.
type = "survival" results in a column .pred_survival.
For the last two types, the results are a nested tibble with an overall
column called .pred with sub-tibbles with the above format.
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.
If "type" is not supplied to predict(), then a choice
is made:
type = "numeric" for regression models,
type = "class" for classification, and
type = "time" for censored regression.
predict() is designed to provide a tidy result (see "Value"
section below) in a tibble output format.
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).
For censored regression, a numeric vector for time is required when
survival or hazard probabilities are requested. Also, when
type = "linear_pred", censored regression models will 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.
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#> # 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#> cyl disp hp drat wt #> Mazda RX4 -0.001433177 -0.8113275 0.6303467 -0.06120265 2.4139815 #> Mazda RX4 Wag -0.001433177 -0.8113275 0.6303467 -0.06120265 1.4488706 #> Datsun 710 -0.009315653 -1.3336453 0.8557288 -0.05014798 3.5494061 #> Hornet 4 Drive -0.001433177 0.1730406 0.6303467 0.12009386 0.1620561 #> Hornet Sportabout 0.006449298 1.1975870 -0.2314083 0.10461733 -0.6895124 #> Valiant -0.001433177 -0.1584303 0.6966356 0.19084372 -0.7652074 #> Duster 360 0.006449298 1.1975870 -1.1594522 0.09135173 -1.1815297 #> Merc 240D -0.009315653 -0.9449204 1.2667197 -0.01477305 0.2566748 #> Merc 230 -0.009315653 -1.0041833 0.8292133 -0.06562451 0.4080647 #> Merc 280 -0.001433177 -0.7349888 0.4579957 -0.06562451 -0.6895124 #> qsec vs am gear carb #> Mazda RX4 -1.567729 0.2006406 2.88774 0.02512680 -0.2497240 #> Mazda RX4 Wag -0.736286 0.2006406 2.88774 0.02512680 -0.2497240 #> Datsun 710 1.624418 -0.3511210 2.88774 0.02512680 0.4668753 #> Hornet 4 Drive 2.856736 -0.3511210 -2.40645 -0.06700481 0.4668753 #> Hornet Sportabout -0.736286 0.2006406 -2.40645 -0.06700481 0.2280089 #> Valiant 4.014817 -0.3511210 -2.40645 -0.06700481 0.4668753 #> Duster 360 -2.488255 0.2006406 -2.40645 -0.06700481 -0.2497240 #> Merc 240D 3.688179 -0.3511210 -2.40645 0.02512680 0.2280089 #> Merc 230 7.993866 -0.3511210 -2.40645 0.02512680 0.2280089 #> Merc 280 1.164155 -0.3511210 -2.40645 0.02512680 -0.2497240 #> attr(,"constant") #> [1] 19.96364