Hi guys!
Ahh, ok, I check it and will confirm you.
thanks!
Shalu
On Wed, Feb 25, 2015 at 9:32 PM, Andy <t3k...@gmail.com> wrote:
> You fit the data again before calling predict_proba.
> You did not fix the random seed, so the outcome of the fit will be
> different and you can't expect it to be consistent.
> Just remove the second call to fit.
>
>
>
> On 02/25/2015 06:35 AM, shalu jhanwar 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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