Feature Engineering Transformations - Interactive Visualization

Feature engineering transforms raw input variables into representations that linear models can use effectively. A log transform converts a right-skewed distribution into a near-Gaussian one. Polynomial features let linear models fit nonlinear patterns. Binning converts continuous variables to ordinal categories. One-hot encoding represents categorical variables as binary indicator columns. These are still the most impactful interventions in a real ML pipeline.

• Apply log transform and watch a right-skewed distribution like income become approximately Gaussian
• Use Box-Cox to find the optimal power transformation automatically - it generalizes the log transform
• Add polynomial features (x^2, x^3, x1*x2) and watch a linear model fit nonlinear relationships
• Bin a continuous feature and see how it can improve model robustness to outliers at the cost of information loss
• Understand target encoding vs one-hot encoding for high-cardinality categoricals - and the data leakage risk in target encoding

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