Understanding AUC Scores in Depth: What’s the Point?
<p>Hello there!</p>
<p>Today, we are delving into a specific metrics used for evaluating model performance — the AUC score. But before we delve into the specifics, have you ever wondered why unintuitive scores are at times necessary to assess the performance of our models?</p>
<p>Whether our model handles a single class or multiple classes, the underlying objective remains constant: optimizing accurate predictions while minimizing incorrect ones. To explore this basic objective, let’s first look at the obligatory confusion matrix encompassing True Positives, False Positives, True Negatives, and False Negatives.</p>
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