Classification is a supervised machine learning task in which a model assigns inputs to discrete categories. It includes binary classification with two classes, multiclass classification with more than two classes, and domain-specific forms such as text classification.
When the data is used to predict a category, supervised machine learning task is called classification. Binary classification refers to predicting only two categories (for example, classifying an image as a picture of either a 'cat' or a 'dog'). Multiclass classification refers to predicting multiple categories (for example, when classifying an image as a picture of a specific breed of dog).
In machine learning, a type of problem that seeks to place (classify) a data sample into a single category or "class." Often, classification problems are modeled to choose one category out of two. These are binary classification problems. Problems with more than two available categories are called "multiclass classification" problems.
A side-by-side comparison of Classification and Regression. Understand how predicting discrete categories differs from predicting continuous or numerical values.
A side-by-side comparison of Clustering and Classification. Understand how grouping similar data points without labels differs from assigning inputs to predefined categories.