Publisher's Synopsis
If you want to learn how decision trees and random forests work, plus create your own, this Machine Learning Algorithms visual book is for you.
The topics covered in this Machine Learning Algorithms 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
