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This assignment dives deeper into machine learning by implementing and evaluating ensemble methods. The notebook covers techniques like bagging and boosting, using models such as Random Forests and AdaBoost. It includes performance comparisons, visualizations, and insights into how ensemble learning improves model accuracy and robustness.
garethltm/MachineLearning-compsci361a2
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This assignment dives deeper into machine learning by implementing and evaluating ensemble methods. The notebook covers techniques like bagging and boosting, using models such as Random Forests and AdaBoost. It includes performance comparisons, visualizations, and insights into how ensemble learning improves model accuracy and robustness.
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