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94predictions.csv		94predictions.csv
README.md		README.md
a2.ipynb		a2.ipynb
predictions.csv		predictions.csv
predictions_compare.py		predictions_compare.py
trg.csv		trg.csv
tst.csv		tst.csv

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361a2

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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.

python supervised-learning ensemble-classifier random-forest-classifier adaboost-classifier bagging-classifier boosting-classifier

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