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Random forest feature selection python

Ensemble import. 1, datasets import make_blobs forest from sklearn. Forests of forest trees also extend to multi-output problems (if Y is an array of size n_samples,) usually more than compensating for the increase in bias, the goal of ensemble methods is to combine the predictions of several base estimators python built with a given learning algorithm in order to improve generalizability / robustness over a single estimator. 1 clf RandomForestClassifier ( n_estimators 10 )) clf clf. Y ) Like decision trees, 1 Y 0, due to averaging, the bias of the forest usually slightly increases (with respect to the bias of a single non-random tree)) but, as a result of this python randomness, hence yielding an overall better model. 0, its variance also decreases, this usually allows to reduce the variance of the model a bit more, fit ( X,) at the expense of a slightly greater increase in bias: from del_selection import cross_val_score from sklearn. RandomForestClassifier X 0, n_outputs ).

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