Classification Algorithms: SVM, kNN, Decision Trees and More

This article explores various classification algorithms, including Support Vector Machines (SVM), k-Nearest Neighbors (kNN), and Decision Trees. Each algorithm possesses distinct advantages and limitations, making algorithm selection crucial based on specific application requirements. For instance, SVM is particularly effective for high-dimensional spaces using kernel functions like linear or RBF, while kNN relies on distance metrics such as Euclidean or Manhattan for instance-based learning. Decision Trees employ feature splitting criteria like Gini impurity or information gain to build interpretable models. We provide comprehensive documentation that explains the underlying mathematical principles, algorithmic workflows, and key implementation considerations. The documentation covers essential aspects like hyperparameter tuning (e.g., SVM's regularization parameter C, kNN's optimal k-value selection) and performance evaluation metrics. Building upon these fundamental algorithms, we further investigate their practical applications in real-world scenarios and discuss optimization techniques to enhance prediction accuracy. These include ensemble methods like Random Forests (extending Decision Trees) and feature engineering approaches that improve algorithm performance. While this article serves as an introduction to basic classification algorithms, we encourage readers to delve deeper into advanced topics such as neural networks, gradient boosting, and algorithm hybridization to develop more effective solutions for complex practical problems.