February 2016
Beginner to intermediate
312 pages
7h 23m
English
Content preview from Regression Analysis with PythonBecome an O’Reilly member and get unlimited access to this title plus top books and audiobooks from O’Reilly and nearly 200 top publishers, thousands of courses curated by job role, 150+ live events each month,
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Regression trees (CART)
A very common learner, recently used very much due to its speed, is the regression tree. It's a non-linear learner, can work with both categorical and numerical features, and can be used alternately for classification or regression; that's why it's often called Classification and Regression Tree (CART). Here, in this section, we will see how regression trees work.
A tree is composed of a series of nodes that split the branch into two children. Each branch, then, can go in another node, or remain a leaf with the predicted value (or class).
Starting from the root (that is, the whole dataset):
- The best feature with which to split the dataset, F1, is identified as well as the best splitting value. If the feature is numerical, ...
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