Hands-On Data Analysis with Pandas

Accuracy and error rate

When our classes are roughly equal in size, we can use accuracy, which will give us the percentage of correctly classified values:

The accuracy_score() function in sklearn.metrics will calculate the accuracy as per the formula; however, the score() method of our model will also give us the accuracy (this isn't always the case, as we will see with grid search in Chapter 10, Making Better Predictions – Optimizing Models):

>>> red_quality_lr.score(r_X_test, r_y_test)0.775

Since accuracy is the percent we correctly classified (our success rate), it follows that our error rate (the percent we got wrong) can be calculated ...