![]() ![]() Let’s illustrate Type I and Type II errors using a binary classification machine learning spam filter. This is also referred to as the False Negative Error. Type II error occurs when a Null Hypothesis that is actually false is accepted.This is also referred to as the False Positive Error. ![]() Type I error occurs when the Null Hypothesis (H0) is mistakenly rejected.Type I and Type II errors are very common in machine learning and statistics.
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