solegalli/feature-selection-in-machine-learning-book
Code repository for the book feature selection in machine learning
This project offers practical code examples for selecting the most relevant variables or columns from your datasets when building predictive models. It takes your raw data with many potential features and helps you identify the strongest predictors, resulting in simpler, more effective models. This is for data scientists, machine learning practitioners, and anyone building predictive analytics who wants to improve model performance and efficiency.
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Use this if you are building machine learning models and need to reduce the number of input variables without losing predictive power, or if you want to understand which features are most important.
Not ideal if you are looking for a fully automated, black-box solution for feature selection without wanting to understand the underlying methods.
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Jupyter Notebook
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Apr 10, 2025
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