![]() You can also use cls.predict_proba() on your data and it gives you the probability of each class prediction by the accumulation of trees and releases you from the pain of going through each tree yourself: x = clf. Print tree_pred #gives you an array of 0 with the predicted class as 1 Print clf.classes_ #gives you the order of the classes Random Forest operates by constructing multiple decision trees at training time. instantiate model with 100 decision trees rf randomforestregressor (nestimators 100, criterionmse, verbose1, randomstate np.random.randomstate (42), njobs -1) train the model on training data rf.fit (trainfeatures, trainlabels) print random forest train score:, rf. ![]() The main principle behind the ensemble methods is that Weak learners can form strong learners. Tree_pred = clf.estimators_.predict_proba(data_test) Random Forest is a supervised learning algorithm that uses an ensemble learning approach for regression and classification. After reading the source code in SKlearn, I realized that we actually have to use predict_proba() instead of predict in the code and it gives you the class that the tree predicts according to the order in clf.classes_. It gave me random numbers instead of the actual class. I had the same issue and I don't know how you got the right answer by using print(clf.estimators_.predict(val.irow(1))).
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