Type I and Type II error in machine learning refers to false values. Type I is equivalent to a False positive while Type II is equivalent to a False negative. In Type I error, a hypothesis which ought to be accepted doesn’t get accepted. Similarly, for Type II error, the hypothesis gets rejected which should have been accepted in the first place.

In machine learning, Type I and Type II errors are associated with binary classification problems:

Type I Error (False Positive):
- Definition: Occurs when the model incorrectly predicts the positive class when it is actually the negative class.
- Example: Classifying a healthy person as having a disease.
Type II Error (False Negative):
- Definition: Occurs when the model incorrectly predicts the negative class when it is actually the positive class.
- Example: Failing to detect a disease in a person who actually has it.

In the context of a confusion matrix:

False Positive (FP): Predicted positive when actual is negative.
False Negative (FN): Predicted negative when actual is positive.

Correct Answer:

Type I Error is related to a situation where you mistakenly conclude something is present (false positive).
Type II Error is related to a situation where you mistakenly conclude something is not present (false negative).

It’s essential to consider both types of errors in the context of the specific problem and the consequences of making each type of mistake. The trade-off between Type I and Type II errors is often managed through adjusting the classification threshold or using evaluation metrics like precision, recall, and F1 score.