Type I and Type II errors are used in machine learning to find the effectiveness of the hypothesis. These are the concepts derived from statistics.
Type I Error: Type I error is the rejection of the null hypothesis. It is also known as a false positive. It means the result indicates that a condition is present but it is not present. E.g. If a test predicts that a person has diabetes, but in reality, the person does not have diabetes. It is an example of a Type I error.
Type II Error: Type II error is the failure to reject the null hypothesis. It is also known as a false negative. It means, the result indicates that a condition is not present, but it is actually present. E.g. If a test predicts that a person does not have diabetes, but in reality the person has diabetes. It is an example of a Type II error.
In machine learning, we have to establish the acceptance criteria of a model on the basis of acceptable false-positive and false-negative results. Therefore, Type I and Type II errors are quite useful in machine learning models.