Feature engineering for numerical and categorical variables: Quick Reference — Data Manipulation and Preparation (NVIDIA-Certified Associate: Accelerated Data Science)

Feature Engineering for Numerical and Categorical Variables — Quick Reference

Feature engineering is a critical step in preparing data for machine learning models, especially when using GPU-accelerated tools like RAPIDS. This quick reference summarizes key concepts, techniques, and best practices for handling numerical and categorical variables efficiently.

1. Numerical Variables

• Definition: Variables representing continuous or discrete numeric values.
• Common Transformations: Scaling (Min-Max, Standardization), normalization, log transforms.
• Handling Outliers: Use clipping or winsorization to limit extreme values.
• Feature Creation: Polynomial features, interaction terms, binning (discretization).
• Missing Values: Impute using mean, median, or GPU-accelerated methods in cuDF or pandas.

2. Categorical Variables

• Definition: Variables representing discrete categories or labels.
• Encoding Techniques:
  • Label Encoding: Assign integer codes to categories; use for ordinal data.
  • One-Hot Encoding: Create binary columns for each category; suitable for nominal data.
  • Frequency Encoding: Replace categories with their frequency counts.
  • Target Encoding: Encode categories based on target variable statistics (use with caution to avoid leakage).
• Handling Rare Categories: Group infrequent categories into an “Other” category to reduce dimensionality.
• Missing Values: Treat as separate category or impute with mode.

3. GPU-Accelerated Tools and Libraries

• cuDF: GPU DataFrame library for fast manipulation and encoding of features.
• RAPIDS Feature Engineering: Use RAPIDS libraries to accelerate transformations and encoding.
• Dask-cuDF: Distributed GPU DataFrames for large-scale feature engineering.

4. Best Practices

• Always fit encoders and scalers on training data only to avoid data leakage.
• Use GPU-accelerated libraries to speed up large dataset transformations.
• Document feature transformations for reproducibility and model interpretability.
• Validate feature importance post-engineering to refine feature sets.

5. Summary Table

Variable Type	Common Techniques	GPU Tools
Numerical	Scaling, Imputation, Binning, Polynomial Features	cuDF, RAPIDS
Categorical	Label Encoding, One-Hot, Frequency, Target Encoding	cuDF, RAPIDS

For detailed documentation and examples, visit the RAPIDS AI official site.

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