ensemble-methods-notebooks
A collection of companion Jupyter notebooks for Ensemble Methods for Machine Learning (Manning, 2023)
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Download Latest Version (.zip)- AdultDataSet-checkpoint.ipynb
- Ch1.3-fit-vs-complexity-checkpoint.ipynb
- Ch1.4-model-averaging-example-checkpoint.ipynb
- Ch2.2and2.3-bagging-and-random-forest-checkpoint.ipynb
- Ch2.3-random-forest-checkpoint.ipynb
- Ch2.5-case-study-breast-cancer-diagnosis-checkpoint.ipynb
- Ch3.1-base-estimators-for-heterogeneous-ensembles-checkpoint.ipynb
- Ch3.2-combining-predictions-by-weighting-checkpoint.ipynb
- Ch3.3-combining-predictions-by-meta-learning-checkpoint.ipynb
- Ch3.4-case-study-sentiment-analysis-checkpoint.ipynb
- Ch4.1and4.2-sequential-ensembles-and-Adaboost-checkpoint.ipynb
- Ch4.3-AdaBoost-in-practice-checkpoint.ipynb
- Ch4.4-case-study-handwritten-digit-classification-checkpoint.ipynb
- Ch4.5-LogitBoost-boosting-wht-the-logistic-loss-checkpoint.ipynb
- Ch4.5-LogitBoost-boosting-with-the-logistic-loss-checkpoint.ipynb
- Ch5.1-gradient-descent-for-minimization-checkpoint.ipynb
- Ch5.2-gradient-boosting - Copy-checkpoint.ipynb
- Ch5.2-gradient-boosting-checkpoint.ipynb
- Ch5.2-TwitchDemo-checkpoint.ipynb
- Ch5.3and5.4-practical-boosting-with-lightgbm-checkpoint.ipynb
- Ch5.5-case-study-document-retrieval-checkpoint.ipynb
- Ch6.1-newtons-method-for-minimization - Copy-checkpoint.ipynb
- Ch6.1-newtons-method-for-minimization-checkpoint.ipynb
- Ch6.2-newton-boosting-checkpoint.ipynb
- Ch6.3and6.4-practical-boosting-with-XGBoost-checkpoint.ipynb
- Ch6.5-case-study-document-retrieval-checkpoint.ipynb
- Ch7.1-regression-checkpoint.ipynb
- Ch7.2-and7.3-parallel-and-sequential-ensembles-for-regression-checkpoint.ipynb
- Ch7.4-case-study-demand-prediction-2-checkpoint.ipynb
- Ch7.4-case-study-demand-prediction-checkpoint.ipynb
- Ch8.1and8.2-encoding-categorical-features_with_category_encoders_and_CatBoost - Copy-checkpoint.ipynb
- Ch8.1and8.2-encoding-categorical-features_with_category_encoders_and_CatBoost-checkpoint.ipynb
- Ch8.3-case-study-income-prediction-checkpoint.ipynb
- Ch8.4-high-cardinality-categories - Copy-checkpoint.ipynb
- Ch8.4-high-cardinality-categories-checkpoint.ipynb
- Ch9.1-glassbox-vs-blackbox-models-checkpoint.ipynb
- Ch9.2-case-study-data-driven-marketing-checkpoint.ipynb
- Ch9.3-black-box-methods-for-global-explainability-checkpoint.ipynb
- Ch9.4and9.5-black-box-methods-for-local-explainability-and-glass-box-methods-checkpoint.ipynb
- Chapter7.1-regression-checkpoint.ipynb
- Chapter7.2-and7.3-parallel-and-sequential-ensembles-for-regression-checkpoint.ipynb
- Chapter7.4-case-study-demand-prediction-checkpoint.ipynb
- Exploring-Categorical-Features-checkpoint.ipynb
- Untitled-checkpoint.ipynb
- labeledBow.feat
- labeledBow.feat
- imdb.vocab
- test.txt
- train.txt
- vali.txt
- test.txt
- train.txt
- vali.txt
- test.txt
- train.txt
- vali.txt
- test.txt
- train.txt
- vali.txt
- test.txt
- train.txt
- vali.txt
- min.txt
- NULL.txt
- Querylevelnorm.txt
- readme.txt
- S1.txt
- S2.txt
- S3.txt
- S4.txt
- S5.txt
- autompg.csv
- bikesharing-day.csv
- bikesharing-hour.csv
- bikesharing.csv
- case-study-results-old.csv
- case-study-results.csv
- adult.csv
- adult.data
- amazon.csv
- amazon_test.csv
- australian-credit.csv
- australian.dat
- case-study-results.csv
- employee_salaries.csv
- bank-additional-full.csv
- bank_marketing_data.pickle
- bank_marketing_models.pickle
- ErrorVsNumEstimators.pickle
- ErrorVsNumEstimators.pickle.bak
- ErrorVsNumLeaves.pickle
- ErrorVsNumLeaves.pickle.bak
- LightGBMMetrics.pickle
- SeqentialVsParallelBagging.pickle
- XGBoostMetrics.pickle
- readme.txt
- GradientBoostingFromScratch.pdf
- Ch1.3-fit-vs-complexity.ipynb
- Ch1.4-model-averaging-example.ipynb
- Ch2.2and2.3-bagging-and-random-forest.ipynb
- Ch2.5-case-study-breast-cancer-diagnosis.ipynb
- Ch3.1-base-estimators-for-heterogeneous-ensembles.ipynb
- Ch3.2-combining-predictions-by-weighting.ipynb
- Ch3.3-combining-predictions-by-meta-learning.ipynb
- Ch3.4-case-study-sentiment-analysis.ipynb
- Ch4.1and4.2-sequential-ensembles-and-Adaboost.ipynb
- Ch4.3-AdaBoost-in-practice.ipynb
- Ch4.4-case-study-handwritten-digit-classification.ipynb
- Ch4.5-LogitBoost-boosting-with-the-logistic-loss.ipynb
- Ch5.1-gradient-descent-for-minimization.ipynb
- Ch5.2-gradient-boosting.ipynb
- Ch5.3and5.4-practical-boosting-with-lightgbm.ipynb
- Ch5.5-case-study-document-retrieval.ipynb
- Ch6.1-newtons-method-for-minimization.ipynb
- Ch6.2-newton-boosting.ipynb
- Ch6.3and6.4-practical-boosting-with-XGBoost.ipynb
- Ch6.5-case-study-document-retrieval.ipynb
- Ch7.1-regression.ipynb
- Ch7.2-and7.3-parallel-and-sequential-ensembles-for-regression.ipynb
- Ch7.2and7.3-parallel-and-sequential-ensembles-for-regression.ipynb
- Ch7.4-case-study-demand-prediction.ipynb
- Ch8.1and8.2-encoding-categorical-features_with_category_encoders_and_CatBoost.ipynb
- Ch8.3-case-study-income-prediction.ipynb
- Ch8.4-high-cardinality-categories.ipynb
- Ch9.1-glassbox-vs-blackbox-models.ipynb
- Ch9.2-case-study-data-driven-marketing.ipynb
- Ch9.3-black-box-methods-for-global-explainability.ipynb
- Ch9.4and9.5-black-box-methods-for-local-explainability-and-glass-box-methods.ipynb
- dependencies.txt
- LICENSE
- plot_utils.py
- README.md
// repository documentation
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