Decision Trees
In this course, you will learn how to build and use decision trees and random forests - two powerful supervised machine learning models.
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Key Concepts
Review core concepts you need to learn to master this subject
Information Gain at decision trees
Gini impurity
Decision trees leaf creation
Optimal decision trees
Decision Tree Representation
Decision trees pruning
Decision Trees Construction
Random Forest overfitting
Information Gain at decision trees
Information Gain at decision trees
When making decision trees, two different methods are used to find the best feature to split a dataset on: Gini impurity and Information Gain. An intuitive interpretation of Information Gain is that it is a measure of how much information the individual features provide us about the different classes.
What you'll create
Portfolio projects that showcase your new skills
How you'll master it
Stress-test your knowledge with quizzes that help commit syntax to memory
Decision Trees
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