After training the training set, the points in the validation set are used to compute the accuracy or error of the classifier. The key insight here is that we know the true labels of every point in the validation set, but we’re temporarily going to pretend like we don’t. We can use every point in the validation set as input to our classifier. We’ll then receive a classification for that point and can now peek at the true label of the validation point to see whether we got it right or not. If we do this for every point in the validation set, we can compute the validation error!
Validation error might not be the only metric we’re interested in. A better way of judging the effectiveness of a machine learning algorithm is to compute its precision, recall, and F1 score.
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