决策树 Resources

Showing items tagged with "决策树"

Implementation of ID3 algorithm with tree-structured results display. Decision trees classify data for predictive purposes. This method constructs decision trees from training data, and if the tree fails to correctly classify all instances, exceptions are added to the training set. The process iterates until an accurate decision set is formed. Key implementations include entropy calculation, information gain optimization, and recursive tree building functions.

MATLAB 195 views Tagged
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In machine learning, Random Forest is a classifier comprising multiple decision trees, where the output class is determined by the majority vote of individual tree predictions. Developed by Leo Breiman and Adele Cutler, this algorithm integrates "Bootstrap aggregating" and "random subspace method" for robust ensemble learning. This translation includes MATLAB-specific implementation insights for decision tree training, feature sampling, and aggregation techniques.

MATLAB 248 views Tagged
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