gen-nn-predict: Predict from a trained neural network

In kindling: Higher-Level Interface of 'torch' Package to Auto-Train Neural Networks

Predict from a trained neural network

Description

Generate predictions from an "nn_fit" object produced by train_nn().

Three S3 methods are registered:

• predict.nn_fit() — base method for matrix-trained models.

• predict.nn_fit_tab() — extends the base method for tabular fits; runs new data through hardhat::forge() before predicting.

• predict.nn_fit_ds() — extends the base method for torch dataset fits.

Usage

## S3 method for class 'nn_fit'
predict(object, newdata = NULL, new_data = NULL, type = "response", ...)

## S3 method for class 'nn_fit_tab'
predict(object, newdata = NULL, new_data = NULL, type = "response", ...)

## S3 method for class 'nn_fit_ds'
predict(object, newdata = NULL, new_data = NULL, type = "response", ...)

Arguments

object	A fitted model object returned by train_nn().
newdata	New predictor data. Accepted forms depend on the method: predict.nn_fit(): a numeric matrix or coercible object. predict.nn_fit_tab(): a data.frame with the same columns used during training; preprocessing is applied automatically via hardhat::forge(). predict.nn_fit_ds(): a torch dataset, numeric array, matrix, or data.frame. If NULL, the cached fitted values from training are returned (not available for type = "prob").
new_data	Legacy alias for newdata. Retained for compatibility.
type	Character. Output type: "response" (default): predicted class labels (factor) for classification, or a numeric vector / matrix for regression. "prob": a numeric matrix of class probabilities (classification only).
...	Currently unused; reserved for future extensions.

Value

• Regression: a numeric vector (single output) or matrix (multiple outputs).

• Classification, type = "response": a factor with levels matching those seen during training.

• Classification, type = "prob": a numeric matrix with one column per class, columns named by class label.

See Also

train_nn()

kindling documentation built on March 3, 2026, 9:07 a.m.