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Model predictions across many sub-models

Source: R/aaa_multi_predict.R, R/boost_tree.R, R/glmnet-engines.R, and 3 more
multi_predict.Rd

For some models, predictions can be made on sub-models in the model object.

Usage

multi_predict(object, ...)

# S3 method for default
multi_predict(object, ...)

# S3 method for `_xgb.Booster`
multi_predict(object, new_data, type = NULL, trees = NULL, ...)

# S3 method for `_C5.0`
multi_predict(object, new_data, type = NULL, trees = NULL, ...)

# S3 method for `_elnet`
multi_predict(object, new_data, type = NULL, penalty = NULL, ...)

# S3 method for `_lognet`
multi_predict(object, new_data, type = NULL, penalty = NULL, ...)

# S3 method for `_multnet`
multi_predict(object, new_data, type = NULL, penalty = NULL, ...)

# S3 method for `_glmnetfit`
multi_predict(object, new_data, type = NULL, penalty = NULL, ...)

# S3 method for `_earth`
multi_predict(object, new_data, type = NULL, num_terms = NULL, ...)

# S3 method for `_torch_mlp`
multi_predict(object, new_data, type = NULL, epochs = NULL, ...)

# S3 method for `_train.kknn`
multi_predict(object, new_data, type = NULL, neighbors = NULL, ...)

Arguments

object

A model_fit object.

...

Optional arguments to pass to predict.model_fit(type = "raw") such as type.

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", or "raw". When NULL, predict() will choose an appropriate value based on the model's mode.

trees

An integer vector for the number of trees in the ensemble.

penalty

A numeric vector of penalty values.

num_terms

An integer vector for the number of MARS terms to retain.

epochs

An integer vector for the number of training epochs.

neighbors

An integer vector for the number of nearest neighbors.

Value

A tibble with the same number of rows as the data being predicted. There is a list-column named .pred that contains tibbles with multiple rows per sub-model. Note that, within the tibbles, the column names follow the usual standard based on prediction type (i.e. .pred_class for type = "class" and so on).