fit: Fitting a model to the data

In thllwg/tpotr: An R-Wrapper for the Python Automated Machine Learning tool that optimizes machine learning pipelines using genetic programming (TPOT)

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

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.