What is classification means

In supervised learning, we have a dataset with features $\mathbf{X}$ and corresponding class labels $y \in \{C_1, C_2, \dots, C_k\}$, where $k$ is the number of classes.

Train a model $f(\mathbf{X}) \to y$ using the labeled data to learn patterns.

For new data $\mathbf{X}_{\text{new}}$, predict $\hat{y} = f(\mathbf{X}_{\text{new}})$.

Examples:

• Binary classification: $k=2$ (e.g., spam/not spam).
• Multiclass: $k>2$ (e.g., digit recognition 0--9).

Common algorithms: logistic regression, decision trees, SVM, neural networks.

Final answer