Categories: AI/ML Research

Detecting and Overcoming Perfect Multicollinearity in Large Datasets

One of the significant challenges statisticians and data scientists face is multicollinearity, particularly its most severe form, perfect multicollinearity. This issue often lurks undetected in large datasets with many features, potentially disguising itself and skewing the results of statistical models. In this post, we explore the methods for detecting, addressing, and refining models affected by […]

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Detect multicollinearity, target leakage, and feature correlation with Amazon SageMaker Data Wrangler

November 16, 2022

In "FAANG"

Scaling to Success: Implementing and Optimizing Penalized Models

This post will demonstrate the usage of Lasso, Ridge, and ElasticNet models using the Ames housing dataset. These models are particularly valuable when dealing with data that may suffer from multicollinearity. We leverage these advanced regression techniques to show how feature scaling and hyperparameter tuning can improve model performance. In…

September 11, 2024

In "AI/ML Research"

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

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.…

September 14, 2024

In "AI/ML Research"

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