Machine Learning With Random Forests And Decision Trees: A Mostly Intuitive Guide, But Also Some Python
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A practical introduction to two widely used machine learning methods, decision trees and random forests. The author explains how a tree divides data into groups by asking a sequence of questions, and how combining many such trees into a forest can improve accuracy and reduce overfitting. The approach is mostly intuitive, relying on plain explanations and worked examples rather than heavy mathematics, while still including some Python code so readers can try the methods on their own data.
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Topics include building trees, judging how good a split is, and using a forest to make predictions. The book suits readers who want to understand these models well enough to apply them, whether they are beginners in data science or programmers exploring machine learning.
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