Binary classification involves categorizing data into two distinct classes or categories. The classifier predicts whether an instance belongs to one class or the other. Examples of binary classification problems include spam email detection (spam or not spam) or tumor classification (malignant or benign).On the other hand, multi-class classification involves categorizing data into more than two classes or categories. The classifier predicts the class to which an instance belongs out of multiple predefined classes. Examples of multi-class classification problems include image recognition (identifying different objects in an image) or sentiment analysis (categorizing text into positive, negative, or neutral sentiments).