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Machine Learning in Python

Decision Trees in Python

Classification algorithms are used to predict outcomes that only have a few possible variations. In this course, you will explore such models, with a focus on decision trees, which produce handy visuals to illustrate the results of your analysis.

Advanced        11 Lessons        150 Minutes        CPD Credits
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About This Course

Predictive models are not only useful for finding numerical values; classification models are used to categorize data into a class or category. In this course, you will explore the utility and composition of decision trees.

You will examine how decision trees make predictions. You will learn how to train a decision tree model and evaluate the results. You will also explore the options for presenting your results in concise, easily understood visuals.

By the end of this course, you will have experience using a powerful predictive tool to classify your data, empowering you to make better and more informed decisions.

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Learning Outcomes

  • Understand the applications of decision trees
  • Understand how decision trees make predictions
  • Train and evaluate a decision tree model
  • Evaluate the model’s performance
  • Visualize the decision tree model


1. Course Introduction
2. How Decision Trees Work
3. Visualizing the Response Feature
4. Visualizing the Predictor Features
5. Creating Dummy Variables
6. Splitting the Data

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7. Training the Model
8. Evaluating Precision
9. Evaluating Recall
10. Visualizing the Decision Tree
11. Pruning the Decision Tree
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