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