please show the code.
On 02/25/2015 04:51 PM, shalu jhanwar wrote:
Hi guys!
I removed refitting the data, but didn't set random_state explicitly.
The same problem persist .Look at these few examples:
Y_true Y_predict Class0_prob. Class1_prob.
1 0 0.28 0.72
0 0 0.32 0.68
0 0 0.41 0.59
1 0 0.41 0.59
1 0 0.48 0.52
1 1 0.57 0.42
Please let me know still am I missing something??
thanks!
Shalu
On Wed, Feb 25, 2015 at 9:53 PM, shalu jhanwar
<shalu.jhanwa...@gmail.com <mailto:shalu.jhanwa...@gmail.com>> wrote:
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
<mailto: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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