gkunapuli/ensemble-methods-notebooks
A collection of companion Jupyter notebooks for Ensemble Methods for Machine Learning (Manning, 2023)
This project provides practical, hands-on examples for applying advanced machine learning techniques to common business and scientific problems. It takes various datasets (like medical records, text, or financial data) and demonstrates how to build more robust predictive models. Data scientists, machine learning engineers, and advanced analytics professionals can use these examples to improve their model performance.
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Use this if you want to understand and implement sophisticated ensemble machine learning methods (like Bagging, Boosting, and Gradient Boosting) to achieve better prediction accuracy on your data.
Not ideal if you are looking for a plug-and-play solution for general data analysis or if you are new to machine learning concepts.
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93
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40
Language
Jupyter Notebook
License
MIT
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Last pushed
Apr 22, 2023
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