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Tree-Based Models
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Decision trees
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626.
Purity of Decision Tree Splits
medium
What does it mean for a decision tree split to be pure?
A
All samples in a node belong to the same class after the split, resulting in zero impurity
B
The split divides the samples into exactly equal-sized groups regardless of their class labels
C
The split uses a feature with zero correlation with all other features in the dataset
D
The split reduces training error to zero for all samples that pass through the left child node
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