Can Random Forests handle missing values?
Yes, tree-based models can handle missing values and categorical data natively compared to distance-based models.
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More FAQs in An Introduction to Decision Trees and Random Forests
A measure of how often a randomly chosen element from the set would be incorrectly labeled if it was randomly labeled according to the distribution of labels in the subset.
Because it combines the predictions of multiple individual models (trees) to improve accuracy and robustness.
A technique that reduces the size of decision trees by removing sections of the tree that provide little power, preventing overfitting.
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