A Forest of Possibilities: Decision Trees and Beyond

Abstract

Decision trees are fundamental in machine learning due to their interpretability and versatility. They are hierarchical structures used for classification and regression tasks, making decisions by recursively splitting data based on features. This abstract explores decision tree algorithms, tree construction, pruning to prevent overfitting, and ensemble methods like Random Forests. Additionally, it covers handling categorical data, imbalanced datasets, missing values, and hyperparameter tuning. Decision trees are valuable for feature selection and model interpretability. However, they have drawbacks, such as overfitting and sensitivity to data variations. Nevertheless, they find applications in fields like finance, medicine, and natural language processing, making them a critical topic in machine learning.