Answer by negas for Unbalanced classification using RandomForestClassifier in...
Use the parameter class_weight='balanced'From sklearn documentation: The balanced mode uses the values of y to automatically adjust weights inversely proportional to class frequencies in the input data...
View ArticleAnswer by Anatoly Alekseev for Unbalanced classification using...
This is really a shame that sklearn's "fit" method does not allow specifying a performance measure to be optimized. No one around seem to understand or question or be interested in what's actually...
View ArticleAnswer by Meena Mani for Unbalanced classification using...
If the majority class is 1, and the minority class is 0, and they are in the ratio 5:1, the sample_weight array should be:sample_weight = np.array([5 if i == 1 else 1 for i in y])Note that you do not...
View ArticleAnswer by alko for Unbalanced classification using RandomForestClassifier in...
You can pass sample weights argument to Random Forest fit methodsample_weight : array-like, shape = [n_samples] or NoneSample weights. If None, then samples are equally weighted. Splits that would...
View ArticleUnbalanced classification using RandomForestClassifier in sklearn
I have a dataset where the classes are unbalanced. The classes are either '1' or '0' where the ratio of class '1':'0' is 5:1. How do you calculate the prediction error for each class and the rebalance...
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