Hi, Shalu,
can you try to not refit the RandomForestClassifier between .predict() and
.predict_proba() or set the random_state and check whether there is still such
a discrepancy?
Best,
Sebastian
> On Feb 25, 2015, at 6:35 AM, shalu jhanwar <shalu.jhanwa...@gmail.com> wrote:
>
> Hey Guys,
>
> I am using Random forest classifier to perform binary classification on my
> dataset. I wanted to have a confidence value of both the classes
> corresponding to each sample. For that purpose, I used "predict_proba" method
> to predict class probabilities for X samples.
> I saw 2-3 strange observations in my samples as below:
>
> S.No. Y_true Y_predicted_forest Class_0_prob Class_1_prob
> 1. 1 0 0.28
> 0.72
> 2. 0 1 0.56
> 0.44
>
> Here, based on the probabilities of classes, the algorithm should provide
> true positives. But it gave wrong predictions in spite of the high
> probability value of each class.
>
> Can anyone please explain this strange observation when the predicted
> probability of class 0 is more than class 1, still the output is class 1 and
> visa-versa?
>
> For further details, I am providing a chunk of my code used:
> #For Random Forest
> clf = RandomForestClassifier(n_estimators=40)
> scores = clf.fit(X_train, y_train).score(X_test, y_test)
> y_pred = clf.predict(X_test)
> #Get proba for each class:
> y_score = clf.fit(X_train, y_train).predict_proba(X_test)
> #Get value of each class as:
> y_score[:,0] - #For 0 class
> y_score[:,1] - #For 1 class
>
> thanks!
> Shalu
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