Machine Learning With Random Forests And Decision Trees - A Visual Guide

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 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