Songyosk/GBFS4MPPML
Official implementation of "Gradient Boosted and Statistical Feature Selection Pipeline for Materials Property Predictions"
This project helps materials scientists and chemists predict material properties based on their chemical data. You provide your chemical data, either as raw chemical structures or with pre-generated features, and it outputs predictions for specific material properties. This is designed for researchers in materials science and chemistry.
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Use this if you need to predict material properties from chemical data using a robust, statistical feature selection and gradient boosting workflow.
Not ideal if you're not working with chemical data or materials property prediction, or if you prefer a different machine learning approach beyond gradient boosting and statistical feature selection.
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Jupyter Notebook
License
MIT
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Last pushed
Feb 29, 2024
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