This books explains how the machine learning algorithm Random Forests works and in doing so also explains how decision trees work.  It is a book written to avoid the problems you often see in mathematics books, in that they are usually either very simple, or very difficult.  Typical explanations you see for this type of material are either all analogies and generalizations or they dive deeply into using this specific Taylor expansion in order to optimize that loss function.

This book avoids those two extremes and attempts to provide the material in a straight forward but detailed way, like you might do when explaining something to an intelligent coworker.  This book is heavily interspersed with explanatory images and detailed examples.

The other upside to this book is that it is only $2.99, and you can buy it here on Amazon, as opposed to the $30-$90 that you frequently see for Machine Learning books.

Topics Covered

The topics covered in this book are

• An overview of decision trees and random forests
• A manual example of how a human would classify a dataset, compared to how a decision tree would work
• How a decision tree works, and why it is prone to overfitting
• How decision trees get combined to form a random forest
• How to use that random forest to classify data and make predictions
• How to determine how many trees to use in a random forest
• Just where does the "randomness" come from
• Out of Bag Errors & Cross Validation - how good of a fit did the machine learning algorithm make?
• Gini Criteria & Entropy Criteria - how to tell which split on a decision tree is best among many possible choices
• And More