From the course: Machine Learning with Python: Decision Trees
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Why and when to use a decision tree - Python Tutorial
From the course: Machine Learning with Python: Decision Trees
Why and when to use a decision tree
- [Man] In order to know when to use a decision tree, we need to understand its strengths and weaknesses. In terms of strengths, decision trees are simple to understand and interpret. The logical structure of a tree is intuitive, and easy to follow. This means we can easily create business rules, based on the structure of a tree. Unlike some other approaches, which work better with either discreet, or continuous features, decision trees are able to handle both types of data, very well. In other words, decision trees are useful, for both classification and regression problems. Decision trees also do very well, in handling missing, noisy and outlier data. This means we don't need to pre-process our data much, before we can use it to build a decision tree model. During each stage of the recursive partitioning process, the feature that reduces impurity the most is chosen. This means that unimportant features, are ignored by…
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