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Max_features In Random Forest. Also isnt feature sampling the USP of random forest. 3102017 Looking at the source code of the Random Forest estimator the search space for the max_features hyperparameter of sklearns RandomForestClassifier seems rather limited. 2212021 The default value is set to 1. But they are in int so I have to turn them into float.
For sklearns Random forest classification module setting max_features to none takes into consideration all the n features for building each tree. Intmax_features n_features intx 202 x01 We have to assume that I want to use only 2 features from the 20. If log2 then max_featureslog2n_features. Max_features helps to find the number of features to take into account in order to make the best split. If float then max_features is a fraction and roundmax_features n_features features are considered at each split. This is misleading if the documentation isnt carefully examined in particular since this value is different for classification which uses sqrtn_features actually leading to a proper random forest.
If auto then max_featuressqrtn_features.
Also isnt feature sampling the USP of random forest. RandomForestClassifier n_estimators max_featuresNone bootstrapFalse random_state1 This is not particularly helpful for classification per se but allows to better understand what is being done under the hood. Max_features maximum number of features random forest considers to split a node. Max_features helps to find the number of features to take into account in order to make the best split. 2682016 Currently setting auto. Min_sample_leaf minimum number of leaves to split an internal node.