Decision trees are one of the most common approaches used in supervised machine learning. Building a decision tree allows you to model complex relationships between variables by mimicking if-then-else decision-making as a naturally occurring human behavior. In this course, instructor Frederick Nwanganga gives you an overview of how to collect, explore, and transform your data in preparation for building decision tree models in Python.
Discover the power of decision trees, what they are, how they are built, and how they quantify impurity within a partition. Get tips from Frederick on building, visualizing, pruning, and using a decision tree in Python including classification trees and regression trees. By the end of this course, you’ll be ready to start making your own models and applying them to different domains.
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Introduction
- Making decisions with Python
- What you should know
- The tools you need
- Using the exercise files
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Decision Trees
- What is a decision tree?
- How is a decision tree built?
- How do classification trees measure impurity?
- How is a regression tree built?
- How to prune a decision tree
- Why and when to use a decision tree
- Chapter quiz
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Working with Classification Trees
- How to build a classification tree in Python
- How to visualize a classification tree in Python
- How to prune a classification tree in Python
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Working with Regression Trees
- How to build a regression tree in Python
- How to visualize a regression tree in Python
- How to prune a regression tree in Python
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Conclusion
- Next steps with decision trees
- Each of the completed notebook contains the codes (and data) for the entire chapter. 02 folder contains the notebook for the entire chapter on classification trees, while 03 folder contains the notebook for the entire chapter on regression trees.
- To suppress verbosity when plotting decision trees, a semi-colon can be used at the end of plotting commands (although some plots in the notebooks has verbosity enabled).