Great work! In this lesson, you learned how to create decision trees and use them to make classifications. Here are some of the major takeaways:

  • Good decision trees have pure leaves. A leaf is pure if all of the data points in that class have the same label.
  • Decision trees are created using a greedy algorithm that prioritizes finding the feature that results in the largest information gain when splitting the data using that feature.
  • Creating an optimal decision tree is difficult. The greedy algorithm doesn’t always find the globally optimal tree.
  • Decision trees often suffer from overfitting. Making the tree small by pruning helps to generalize the tree so it is more accurate on data in the real world.

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