kennethleungty/Simulated-Annealing-Feature-Selection
Feature Selection using Simulated Annealing
This project helps data scientists and machine learning engineers refine their predictive models. By intelligently selecting only the most impactful input variables from a dataset, it streamlines the modeling process. You provide a dataset with many potential predictor variables, and it outputs a curated list of the best features, leading to more efficient and accurate machine learning models.
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Use this if you are building machine learning models and want to improve their performance and reduce training time by focusing on the most relevant data.
Not ideal if you need a simple, fast feature selection method for a small dataset, or if your primary goal is interpretability rather than predictive power.
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Jupyter Notebook
License
MIT
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Last pushed
Aug 10, 2022
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