Automating Feature Engineering

Home Posts Tagged "Automating Feature Engineering"
Thoughts, ideas, and tutorials on automating feature engineering.
                
    Boruta
Boruta(SHAP) Does Not Work For The Reason You Think It Does!
            Everything you wish you knew about Boruta, and more.
        
        Yves-Laurent Kom Samo, PhD
        
    3 May 2022
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    8 min read
    
    Common Pitfalls
Autoencoders: What Are They, and Why You Should Never Use Them For Pre-Processing
            Fundamental limitations you need to be aware of before using autoencoders as pre-processing step in predictive modeling problems on tabular data.
        
        Yves-Laurent Kom Samo, PhD
        
    19 Apr 2022
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    14 min read
    
    Principal Feature Selection
How To Fix PCA To Make It Work For Feature Selection
            Introducing Principal Feature Selection (With Code)
        
        Yves-Laurent Kom Samo, PhD
        
    8 Apr 2022
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    9 min read
    
    Feature Selection
Feature Engineering With Game Theory: Beyond SHAP values
            Understanding the difference between feature importance, feature usefulness, and feature potential using Shapley values.
        
        Yves-Laurent Kom Samo, PhD
        
    23 Mar 2022
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    7 min read
    
    Common Pitfalls
5 Reasons Why You Should Never Use Recursive Feature Elimination
            Fundamental limitations you need to be aware of before using Recursive Feature Elimination (RFE) or any other feature selection algorithm based on feature importance.
        
        Yves-Laurent Kom Samo, PhD
        
    15 Mar 2022
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    4 min read
    
    Model Compression
AutoML: How To Reduce Your Model Size by 95% While Improving Model Performance
            We show you how to reduce the number of features used by AWS' AutoGluon tabular models in Python by 95% while improving model performance.
        
        Yves-Laurent Kom Samo, PhD
        
    8 Mar 2022
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    10 min read
    
    Model Compression
Random Forest: How To Reduce Your Production Model Size by 95%
            We show you how to reduce the number of features used by your Random Forest model in Python by 95%, at no performance cost.
        
        Yves-Laurent Kom Samo, PhD
        
    8 Mar 2022
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    7 min read
    
    Model Compression
XGBoost: How To Reduce Your Production Model Size by 95%
            We show you how to reduce the number of features used by your XGBoost model in Python by 95%, at no performance cost.
        
        Yves-Laurent Kom Samo, PhD
        
    7 Mar 2022
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    7 min read
    
    Model Compression
LightGBM: How To Reduce Your Production Model Size by 95%
            We show you how to reduce the number of features used by your LightGBM model in Python by 95%, at no performance cost.
        
        Yves-Laurent Kom Samo, PhD
        
    6 Mar 2022
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    7 min read
    
    Model Compression
How To Seamlessly Compress Any Tabular Model in Python
            Train your favorite predictive models in Python (e.g. LightGBM, XGBoost, Scikit-Learn, Tensorflow, and PyTorch models) using at least 80% fewer features, at no performance cost.
        
        Yves-Laurent Kom Samo, PhD
        
    4 Feb 2022
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    18 min read
    
    Automating Feature Engineering
Effective Feature Selection: Beyond Shapley Values, Recursive Feature Elimination (RFE) and Boruta
            We explain why feature selection matters, why RFE, Boruta, and SHAP values aren't good enough, and we propose an information-theoretical alternative.
        
        Yves-Laurent Kom Samo, PhD
        
    21 Jan 2022
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    12 min read
    
    Automating Feature Engineering
Feature Engineering: Why It Matters, and How To Do It Right
            We explain why every great model needs great features, and we arm you with tools and principles that will help you create better features.
        
        Yves-Laurent Kom Samo, PhD
        
    16 Dec 2021
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    6 min read