发帖
雷达卡
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
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