Applying Decision Trees

Learn how to apply and tweak decision trees.


  • Learn how to train a decision tree model using Scikit-learn.
  • Learn how to evaluate error using AUC.
  • Learn how to reduce overfitting with decision trees.

Mission Outline

1. Introduction to the Data Set
2. Using Decision Trees With scikit-learn
3. Splitting the Data into Train and Test Sets
4. Evaluating Error With AUC
5. Computing Error on the Training Set
6. Decision Tree Overfitting
7. Reducing Overfitting With a Shallower Tree
8. Tweaking Parameters to Adjust AUC
9. Tweaking Tree Depth to Adjust AUC
10. Underfitting in Simplistic Trees
11. The Bias-Variance Tradeoff
12. Exploring Decision Tree Variance
13. Pruning Leaves to Prevent Overfitting
14. Knowing When to Use Decision Trees
15. Takeaways


Course Info:

Decision Trees


The median completion time for this course is 6.4 hours.

This course requires a premium subscription. This course has four missions and one guided project.  It is the 22nd course in the Data Scientist in Python path.


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