English | MP4 | AVC 1280×720 | AAC 44KHz 2ch | 1h 13m | 194 MB
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.
Table of Contents
Introduction
1 Making decisions with Python
2 What you should know
3 The tools you need
4 Using the exercise files
Decision Trees
5 What is a decision tree
6 How is a classification tree built
7 How do classification trees measure impurity
8 How is a regression tree built
9 How to prune a decision tree
10 Why and when to use a decision tree
Working with Classification Trees
11 How to build a classification tree in Python
12 How to visualize a classification tree in Python
13 How to prune a classification tree in Python
Working with Regression Trees
14 How to build a regression tree in Python
15 How to visualize a regression tree in Python
16 How to prune a regression tree in Python
Conclusion
17 Next steps with decision trees
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