Explain how a Decision Tree works in the context of Machine Learning.
- Based on complexity, combines data at each node
- Based on distance, groups data at each node
- Based on entropy, splits data at each node
- Based on gradient, organizes data at each node
A Decision Tree works by splitting the data into subsets based on feature values. This is done recursively at each node by selecting the feature that provides the best split according to a metric like entropy or Gini impurity. The process continues until specific criteria are met, creating a tree-like structure.
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