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What do you understand by Type I and Type II errors in machine learning?

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Type I Error: Type I error (False Positive) is an error where the outcome of a test shows the non-acceptance of a true condition.

For example, a cricket match is going on and, when a batsman is not out, the umpire declares that he is out. This is a false positive condition. Here, the test does not accept the true condition that the batsman is not out.

Type II Error: Type II error (False Negative) is an error where the outcome of a test shows the acceptance of a false condition.

For example, the CT scan of a person shows that he is not having a disease but, in reality, he is having it. Here, the test accepts the false condition that the person is not having the disease.

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