workflows: predict-workflow – R documentation

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predict-workflow

Predict from a workflow

Description

This is the predict() method for a fit workflow object. The nice thing about predicting from a workflow is that it will:

Preprocess new_data using the preprocessing method specified when the workflow was created and fit. This is accomplished using hardhat::forge(), which will apply any formula preprocessing or call recipes::bake() if a recipe was supplied.
Call parsnip::predict.model_fit() for you using the underlying fit parsnip model.

Usage

## S3 method for class 'workflow'
predict(object, new_data, type = NULL, opts = list(), ...)

Arguments

`object`	A workflow that has been fit by `fit.workflow()`
`new_data`	A data frame containing the new predictors to preprocess and predict on
`type`	A single character value or `NULL`. Possible values are "numeric", "class", "prob", "conf_int", "pred_int", "quantile", or "raw". When `NULL`, `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.
`...`	Arguments to the underlying model's prediction function cannot be passed here (see `opts`). There are some `parsnip` related options that can be passed, depending on the value of `type`. Possible arguments are: `level`: for `type`s of "conf_int" and "pred_int" this is the parameter for the tail area of the intervals (e.g. confidence level for confidence intervals). Default value is 0.95. `std_error`: add the standard error of fit or prediction (on the scale of the linear predictors) for `type`s of "conf_int" and "pred_int". Default value is `FALSE`. `quantile`: the quantile(s) for quantile regression (not implemented yet) `time`: the time(s) for hazard probability estimates (not implemented yet)

Value

A data frame of model predictions, with as many rows as new_data has.

Examples

library(parsnip)
library(recipes)
library(magrittr)

training <- mtcars[1:20,]
testing <- mtcars[21:32,]

model <- linear_reg() %>%
  set_engine("lm")

workflow <- workflow() %>%
  add_model(model)

recipe <- recipe(mpg ~ cyl + disp, training) %>%
  step_log(disp)

workflow <- add_recipe(workflow, recipe)

fit_workflow <- fit(workflow, training)

# This will automatically `bake()` the recipe on `testing`,
# applying the log step to `disp`, and then fit the regression.
predict(fit_workflow, testing)

workflows

Modeling Workflows

v0.2.2

MIT + file LICENSE

Authors

Davis Vaughan [aut, cre], RStudio [cph]

Initial release