October 2018
Intermediate to advanced
172 pages
4h 6m
English
Content preview from Machine Learning with scikit-learn Quick Start Guide
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Hyperparameter tuning for the decision tree
The decision tree has a plethora of hyperparameters that require fine-tuning in order to derive the best possible model that reduces the generalization error as much as possible. In this section, we will focus on two specific hyperparameters:
- Max depth: This is the maximum number of children nodes that can grow out from the decision tree until the tree is cut off. For example, if this is set to 3, then the tree will use three children nodes and cut the tree off before it can grow any more.
- Min samples leaf: This is the minimum number of samples, or data points, that are required to be present in the leaf node. The leaf node is the last node of the tree. If this parameter is, for example, set to ...
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