Yimeng-Zhang/feature-engineering-and-feature-selection
A Guide for Feature Engineering and Feature Selection, with implementations and examples in Python.
This guide helps data scientists and machine learning practitioners improve their models by focusing on the raw data. It takes your initial datasets and provides strategies and code examples to create better input features, ultimately leading to more accurate and robust machine learning models. The target audience is anyone building machine learning models who wants to enhance their data preparation workflow.
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Use this if you are a data scientist or machine learning engineer struggling to get good performance from your models and suspect that the quality or representation of your input data features might be the bottleneck.
Not ideal if you are looking for a fully automated, black-box solution or if your primary interest is in advanced deep learning architectures rather than foundational data preparation.
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Sep 24, 2022
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