Another possibility is to threshold the predict_proba differently, such
that the decision maximizes whatever metric you have defined.
On 03/15/2016 07:44 AM, Mamun Rashid wrote:
Hi All,
I have asked this question couple of weeks ago on the list. I have a
two class problem where my positive class ( Class 1 ) and negative
class ( Class 0 )
is imbalanced. Secondly I care much less about the negative class. So,
I specified both class weight (to a random forest classifier) and
sample wright to
the fit function to give more importance to my positive class.
cl_weight = {0:weight1,1:weight2}
clf= RandomForestClassifier(n_estimators=400, max_depth=None,
min_samples_split=2, random_state=0, oob_score=True, class_weight =
cl_weight, criterion=*“g**ini*")
sample_weight = np.array([weightif m ==1 else 1 for min df_tr[label_column]])
y_pred = clf.fit(X_tr, y_tr,sample_weight= sample_weight).predict(X_te)
Despite specifying dramatically different class weight I do not
observe much difference. Example :: cl_weight = {0:0.001, 1:0.999} and
cl_weight = {0:0.50, 1:0.50}. Am I passing the class weight correctly ?
I am giving example of two folds from these two runs :: Fold 1 and
Fold 2.
## cl_weight = {0:0.001, 1:0.999}
Fold_1 Confusion Matrix 0 1 0 1681 26 1 636 149 Fold_5 Confusion
Matrix 0 1 0 1670 15 1 734 160 ## cl_weight = {0:0.50, 1:0.50}
Fold_1 Confusion Matrix 0 1 0 1690 15 1 630 163 Fold_5 Confusion
Matrix 0 1 0 1676 14 1 709 170
Thanks,
Mamun
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