Beyond The VIF: Collinearity Analysis for Bias Mitigation and Predictive Accuracy

In machine learning, collinearity is a complex puzzle to both seasoned professionals and newbies alike. Machine learning (ML) algorithms are optimised for predictive accuracy not explainability of predictors on the target. Also, most solutions for addressing collinearity, such as the ‘Variance Inflation Score’, and ‘Pearson’s cross-correlation analysis’ potentially leads to massive information loss in pre-processing.

Most machine learning algorithms will select the best possible combination of features to optimize predictive accuracy. Therefore, even with collinearity, …

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