I have been using the sonar data set (I believe this is a sample data set
used in many demonstrations of machine learning.) It is a two class data
set with 60 features with 208 training examples.
I have a questions about using sample weights in fitting the SVM model.
When I fit the model using scaled data, I get a test error of 10.3%. When I
fit the model using a sample weight vector of 1/N, I get a test error of
37%.
Here is the code:
w=np.ones(len(y_train))
clf=svm.SVC(kernel='rbf', C=10, gamma=.01)
clf.fit(x_tr_scaled,y_train)
score_scaled_tr=clf.score(x_tr_scaled,y_train)
score_scaled_test=clf.score(x_te_scaled,y_test)
w=w/sum(w)
clf1=svm.SVC(kernel='rbf', C=10, gamma=.01, probability=True)
clf1.fit(x_tr_scaled,y_train,sample_weight=w)
print "Training score with sample weights is ", clf1.score(x_tr,y_train)
print "Score with sample weights is", clf1.score(x_te_scaled,y_test)
What am I doing wrong here?
Also, when I tried this command:
Pr=predict_proba(x_tr_scaled)
I get the error that predict_proba is an undefined name. However, I got it
from this link:
http://scikit-learn.org/stable/modules/generated/sklearn.svm.SVC.html#sklearn.svm.SVC
Any help would be appreciated.
Anne Dwyer
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