CODEWITHSUNDEEP
pythonmachine-learningscikit-learnclassification
linkyndy
linkyndy

Reputation: 17928

Evaluate a prediction's accuracy

I've trained and tested my KNN classifier. I've crossed validated it and the mean score is decent. Now, I would like to predict labels for some real data. But is there a way of seeing the accuracy of a prediction? I would like to actually save the predicted label only if the accuracy is high enough. I'm using Python and scikit-learn.

Upvotes: 1

Views: 1547

Answers (1)

RPresle
RPresle

Reputation: 2561

As @jonrsharpe said, predict_proba should do it.

It provide an array with all the probabilities of the categories. Lets say, you have 3 categories [A, B, C]. The predict_proba method will return [0.2,0.3,0.5]. So here your accuracy would be 

A=0.2
B=0.3
C=0.5

For example :

categories = [A, B, C]
X = # put your data
Y = # put your result

classifier.fit(X, Y)
prediction = classifier.predict_proba(X) # predict whatever you want here

for line in prediction:
    # numpy.argmax return the index of the biggest value in the array
    # max return the biggest value
    print("The class is %s with proba : %f " % (categories[numpy.argmax(line)], max(line, key=float)))

Important : take care of the order in your categories array. The content of the predict_proba results are sorted lexically based on the categories. Don't hesitate to sort categories before treating the result

Upvotes: 2

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