positive and negative samples

In the context of machine learning, positive and negative samples are used to teach computers to distinguish between different types of data. Positive samples are examples that contain the feature or condition we're trying to identify, like images of cats when training a cat detector. Negative samples are those that do not have that feature, such as images without cats. By processing both types of samples, the computer learns to recognize patterns associated with the positive class and differentiate it from the negatives, improving its accuracy in tasks like classification or detection.