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Tag: ML

  • Filling the Gaps: A Comparative Guide to Imputation Techniques in Machine Learning

    Filling the Gaps: A Comparative Guide to Imputation Techniques in Machine Learning

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    Annick N’dri

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    In our previous exploration of penalized regression models such as Lasso, Ridge, and ElasticNet, we demonstrated how effectively these models manage multicollinearity, allowing us to utilize a broader array of features to enhance model performance. Building on this foundation, we now address another crucial aspect of data preprocessing—handling missing values. Missing data can significantly compromise…

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  • Industries in Focus: Machine Learning for Cybersecurity Threat Detection

    Industries in Focus: Machine Learning for Cybersecurity Threat Detection

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    Annick N’dri

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    Cybersecurity threats are becoming increasingly sophisticated and numerous. To address these challenges, the industry has turned to machine learning (ML) as a tool for detecting and responding to cyber threats. This article explores five key ML models that are making an impact in cybersecurity threat detection, examining their applications and effectiveness in protecting digital assets.…

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  • Exploring LightGBM: Leaf-Wise Growth with GBDT and GOSS

    Exploring LightGBM: Leaf-Wise Growth with GBDT and GOSS

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    Annick N’dri

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    LightGBM is a highly efficient gradient boosting framework. It has gained traction for its speed and performance, particularly with large and complex datasets. Developed by Microsoft, this powerful algorithm is known for its unique ability to handle large volumes of data with significant ease compared to traditional methods. In this post, we will experiment with…

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