Classification Algorithms

Introduction

Classification is a supervised learning task where a model assigns inputs to discrete categories, such as spam or not spam, or which digit an image shows. The model learns from labeled examples and then predicts a class label for new data. It is one of the most common and useful ML tasks across industries.

Definition

Classification algorithms are used to categorize input data into predefined classes or categories based on their features.

Types

Binary Classification

Classifying data into two categories

Multiclass Classification

Classifying data into three or more categories

Multilabel Classification

Assigning multiple labels to each instance

Hierarchical Classification

Classification with hierarchical class relationships

Use Cases

• Email spam detection
• Medical diagnosis
• Image recognition
• Sentiment analysis
• Credit risk assessment

In Practice

Classifiers range from logistic regression and decision trees to support vector machines and neural networks. Performance is judged with metrics like accuracy, precision, recall, and F1 score, and the right metric depends on the cost of false positives versus false negatives in your application.

Key Points

• Requires labeled training data
• Can handle both linear and non-linear relationships
• Performance depends on feature quality
• Different algorithms suit different problems

References

• Scikit-learn Classification Guide — Comprehensive guide to classification algorithms in scikit-learn