CODEWITHSUNDEEP
pythonmachine-learningscikit-learnsvmprediction
Leandro Baruch
Leandro Baruch

Reputation: 117

How to know how many is class 0 and how many is class 1?

I have a code that gives me the accuracy of a SVM, but I want to know how many is class 0 and 1.

Here is the code

from sklearn.svm import SVC
from sklearn.metrics import accuracy_score

clf = SVC(C=10000.0, kernel='rbf')
t0 = time()
clf.fit(features_train, labels_train)
print "training_time:", round(time()-t0, 3), "s"
t0 = time()
pred = clf.predict(features_test)
print "prediction time:", round(time()-t0, 3), "s"
acc = accuracy_score(pred, labels_test)
print acc

I have tried this code below, but no success...

from sklearn.svm import SVC
from sklearn.metrics import accuracy_score

clf = SVC(C=10000.0, kernel='rbf', probability=True)
t0 = time()
clf.fit(features_train, labels_train)
print "training_time:", round(time()-t0, 3), "s"
t0 = time()
pred = clf.predict(features_test)
class = clf.predict_proba(features_test)
print sum(class)
print "prediction time:", round(time()-t0, 3), "s"
acc = accuracy_score(pred, labels_test)
print acc

What am I missing? Ty!

Upvotes: 0

Views: 194

Answers (1)

Venkatachalam
Venkatachalam

Reputation: 16966

You can create the confusion matrix to understand your prediction

from sklearn.metrics import confusion_matrix
confusion_matrix(labels_test, pred)

Upvotes: 1

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