Machine Learning
Supervised Learning: Classification / Introduction to Classification
1. Introduction to Classification
Classification is a type of supervised machine learning task where the goal is to predict the categorical class label of new observations based on past observations (training data). The algorithm learns a mapping function f: X → Y, where X represents input features and Y represents discrete class labels.
Definition
Given a set of training examples, each labeled with one of K classes, learn a classifier that maps new examples to one of these K classes.
Types of Classification
Binary Classification
Two class labels. Example: Email spam detection (Spam / Not Spam).
Multi-class Classification
More than two classes. Example: Handwritten digit recognition (0–9).
Multi-label Classification
Each instance can belong to multiple classes simultaneously. Example: Image tagging.
Imbalanced Classification
Class distribution is highly skewed. Example: Fraud detection (99.9% legit, 0.1% fraud).