The answer
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.
An Introduction to Decision Trees and Random Forests
What is Gini Impurity?
More FAQs in An Introduction to Decision Trees and Random Forests
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.
Usually between 100 and 500 trees are sufficient. Adding more trees improves accuracy but increases compute cost.
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