Scikit-学习predict_proba的RandomForestClassifier输出

Question

I have a dataset that I split in two for training and testing a random forest classifier with scikit learn .

I have 87 classes and 344 samples. The output of predict_proba is, most of the times, a 3-dimensional array (87, 344, 2) (it's actually a list of 87 numpy.ndarray s of (344, 2) elements).

Sometimes, when I pick a different subset of samples for training and testing, I only get a 2-dimensional array (87, 344) (though I can't work out in which cases).

My two questions are:

• what do these dimensions represent? I worked out that to get a ROC AUC score, I have to take one half of the output (that is (87, 344, 2)[:,:,1] , transpose it, and then compare it with my ground truth ( roc_auc_score(ground_truth, output_of_predict_proba[:,:,1].T) essentially) . But I don't understand what it really means.
• why does the output change with different subsets of the data? I can't understand in which cases it returns a 3D array and in which cases a 2D one.

Answer 1

classifier.predict_proba() returns the class probabilities. The n dimension of the array will vary depending on how many classes there are in the subset you train on

Answer 2

Are you sure the arrays you're using to fit the RF has the right shape ? (n_samples,n_features) for the data and (n_samples) for the target classes. You should get an array Y_pred of shape (n_samples,n_classes) so (344,87) in your case, where item i of row r is the predictied probability of the class i for the sample X[r,:]. Note that sum( Y_pred[r,:] ) = 1 .

However I think if your target array Y has shape (n_samples,n_classes), where each row would be all zeros except one corresponding to the class of the sample, then sklearn take it as a multi-output prediction problem (consider each class independently) but I don't think that's what you'd like to do. In that case, for each class and each sample, you would predict the probability of belonging to this class or not.

Finally the output indeed depend on the training set because it depends on the number of classes (in the training set). You can get it with the attribute n_classes (and you may also be able to force the number of classes by setting it manually) and you can also get the classes' value with the attribute classes . See the documentation .

Hope it helps !