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. Missing data can significantly compromise the accuracy and reliability of models if not appropriately managed. This post explores various imputation strategies to address missing data and embed them into our pipeline. This approach allows us to further refine our predictive accuracy by incorporating previously excluded features, thus making the most of our rich dataset.
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