fit: Fitting a model to the data

Description Usage Arguments Value

Description

Uses genetic programming to optimize a machine learning pipeline that maximizes score on the provided features and target. Performs internal k-fold cross-validaton to avoid overfitting on the provided data. The best pipeline is then trained on the entire set of provided samples.

Usage

1
fit(obj, features, target, sample_weight = NULL, group = NULL)

Arguments

obj

A TPOTClassifier or a TPOTRegressor

target

List of class labels for prediction

group

Group labels for the samples used when performing cross-validation. This parameter should only be used in conjunction with sklearn's Group cross-validation functions, such as sklearn.model_selection.GroupKFold

feature

A data.frame of observations

sample_weights

Per-sample weights. Higher weights indicate more importance. If specified, sample_weight will be passed to any pipeline element whose fit() function accepts a sample_weight argument. By default, using sample_weight does not affect tpot's scoring functions, which determine preferences between pipelines.

Value

Returns a copy of the fitted TPOT Object


thllwg/tpotr documentation built on July 5, 2019, 12:49 a.m.