StatguyUser/feature_engineering_and_selection_for_explanable_models
Code repository for the machine learning book Feature Engineering & Selection for Explainable Models: A Second Course for Data Scientists
This project provides practical methods to transform raw data into a structured format and select the most relevant variables for building predictive models. It helps data scientists prepare their datasets more effectively, making the resulting models easier to understand and trust. You'll input your raw business data and learn how to generate meaningful features that improve model performance and explainability.
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Use this if you are a data scientist who needs to improve the predictive power and interpretability of your machine learning models by carefully crafting and selecting relevant features from your raw data.
Not ideal if you are looking for a fully automated, black-box solution for model building without needing to understand or manipulate the underlying features.
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Aug 06, 2023
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