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1) How can I change classification threshold (i think it is 0.5 by default) in RandomForest in sklearn?

2) how can I under-sample in sklearn?

3) I have the following result from RandomForest classifier: [[1635 1297] [ 520 3624]]

         precision    recall  f1-score   support

class 0       0.76      0.56      0.64      2932
class 1       0.74      0.87      0.80      4144

avg / total 0.75 0.74 0.73 7076

first, data is unbalanced (30% from class-0 and 70% from class-1). So, I think the classifier is more likely to be biased for class-1 meaning move some from class-0 to class-1 (there are 1297 missclassification for class-0 but 520 missclassification for class-1). How can I fix this? if downsampling can help? or changing classification threshold?

Update: class-0 has 40% of population while class-1 is 60%. However, drift from class-0 to class-1 (1297) is high while I want this becomes low.

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You could indeed wrap you random forest in a class that a predict methods that calls the predict_proba method of the internal random forest and output class 1 only if it's higher than a custom threshold.

Alternatively you can bias the training algorithm by passing a higher sample_weight for samples from the minority class.

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  • $\begingroup$ Thanks. I was thinking about increasing weight for minor class. However, I can not see anything in RandomForest classifier (there is in SGDclassifier) $\endgroup$ – Big Data Lover Dec 18 '13 at 23:01
  • $\begingroup$ The fit method accept a sample_weight param (one weight per parameter) that is very flexible and makes it possible to simulate class_weight (one weight per target class). $\endgroup$ – ogrisel Dec 19 '13 at 8:48
  • $\begingroup$ Thanks. when I use clf = clf.fit(X, Y, sample_weight=preprocessing.balance_weights(y) it gives me ValueError: operands could not be broadcast together with shapes. y is binary 0/1 $\endgroup$ – Big Data Lover Dec 19 '13 at 22:52
  • $\begingroup$ What is the shape of y? Why do you have different Y and y? $\endgroup$ – ogrisel Dec 20 '13 at 16:22

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