Model predictions across many sub-models — multi

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

Usage

multi_predict(object, ...)

# Default S3 method
multi_predict(object, ...)

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

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

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

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

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

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

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

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

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

Arguments

object: A model fit.
...: 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).