Precision: Fraction of positive predictions that are actually positive

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In many real-world settings, accuracy or error are not the best quality metrics for classification. You will explore a case-study that significantly highlights this issue: using sentiment analysis to display positive reviews on a restaurant website. Instead of accuracy, you will define two metrics: precision and recall, which are widely used in real-world applications to measure the quality of classifiers. You will explore how the probabilities output by your classifier can be used to trade-off precision with recall, and dive into this spectrum, using precision-recall curves. In your hands-on implementation, you will compute these metrics with your learned classifier on real-world sentiment analysis data.

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