LeanML Feature Selection

Home Posts Tagged "LeanML Feature Selection"
Articles related to the information-theoretic LeanML feature selection algorithm.
                
    LeanML Feature Selection
How I Got In The Top 1% of A Kaggle Competition With kxy And No Hyper-Parameter Tuning
            The solution that ranked 26th/1946 in the G-Research Crypto Forecasting Kaggle competition.
        
        Yves-Laurent Kom Samo, PhD
        
    9 May 2022
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    6 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