Answer Posted / Lubhanshu Bartaria

In classification tasks, false positives (Type I errors) occur when the model incorrectly classifies a negative instance as positive. False negatives (Type II errors) occur when the model incorrectly classifies a positive instance as negative. These concepts are crucial because they measure the performance of a classifier, specifically its precision and recall.

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