You want to reduce the variance of a single Decision Tree without switching to a completely different algorithm family. Which strategy best addresses this using concepts you already know about Decision Trees?

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Answer

Train multiple Decision Trees on different bootstrap samples of the data and average their predictions (bagging)

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Train one very deep Decision Tree and increase min_samples_leaf to 1

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Increase the number of features used at each split to include all available features

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Train one Decision Tree and use it to generate synthetic training data for a second tree

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You want to reduce the variance of a single Decision Tree without switching to a...

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