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Supervised Learning
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Tree-Based Models
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Decision trees
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306.
Full-Depth Tree Bias and Variance
easy
A decision tree is grown to full depth on a training set. What best describes its bias and variance?
A
Low bias and low variance — full depth trees are the optimal configuration for all datasets
B
High bias and high variance — full depth trees simultaneously underfit and overfit different regions
C
Low bias and high variance — it fits training data almost perfectly but is sensitive to small changes
D
High bias and low variance — it underfits the data by making overly simple partitions of the feature space
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