Description Usage Arguments Value
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.
1 |
obj |
A |
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 |
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. |
Returns a copy of the fitted TPOT Object
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