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Hands-On Quantum Machine Learning with Python

Performance Evaluation of Hypocrite Classifier

Explore how to assess the performance of hypocrite classifiers using key evaluation metrics including precision, recall, specificity, and negative predictive value. Understand how these metrics reflect classifier tendencies and learn to interpret complementary measures to avoid misleading conclusions. This lesson guides you through calculating, visualizing, and summarizing classifier performance for binary classification challenges.

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Let’s see how the different hypocrite classifiers perform at the four metrics depending on the weight.

Metrics of the hypocrite classifier

l_precision = list(map(lambda step: precision_score(train_labels, l_predictions[step]),steps))
l_recall = list(map(lambda step: recall_score(train_labels, l_predictions[step]),steps))
l_specificity = list(map(lambda step: specificity(l_cm[step]),steps))
l_npv = list(map(lambda step: npv(l_cm[step]),steps))

In these ...