Decision Trees Decoded
Master the Algorithm That Powers Machine Learning's Most Interpretable Models
You'll gain the expertise to design, implement, and optimize decision tree algorithms that solve real-world problems with clarity and precision. This book bridges the gap between theoretical computer science and practical machine learning, giving you a deep understanding of how recursive partitioning creates powerful predictive models. You'll explore the mathematical foundations of impurity measures, learn why certain splits outperform others, and discover how to prevent overfitting through intelligent pruning strategies. Beyond single trees, you'll master ensemble techniques that combine multiple trees into robust, high-performance systems. Each concept builds naturally on the previous one, moving from basic binary splits to advanced topics like handling missing data, feature importance analysis, and computational optimization. With clear explanations of algorithms, complexity analysis, and decision-making frameworks, you'll develop the confidence to choose the right tree-based approach for your specific use case. Whether you're building classification systems, regression models, or interpretable AI solutions, this book equips you with the knowledge to leverage decision trees effectively and understand exactly why your models make the predictions they do.
Est. Length
222 pages (12 chapters)
Language
English
Publication date
Oct 21, 2025
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