Does Khiops Automatically Apply Log, Square, or Other Transformations During Variable Encoding?

[lucaurelien]

This question was raised during a presentation of Khiops.

[lucaurelien]

No, Khiops does not automatically apply transformations such as logarithm, square, or square root based on skewness or other distributional characteristics. Instead, it relies on a non-parametric, information-theoretic discretization approach based on value ranks, making explicit variable transformations unnecessary.

Why Are Such Transformations Unnecessary in Khiops?

Khiops employs the MODL (Minimum Optimized Description Length) discretization method, which ensures:

• Optimal Binning via Information Theory: The MODL criterion selects the best discretization scheme with the optimal number of intervals and bounds, without requiring parametric assumptions. It directly adapts to the data’s intrinsic structure.
• Invariance to Monotonic Transformations: Khiops encodes numerical variables using value ranks rather than raw values. Thus, monotonic transformations (log, square root, exponential, etc.) have no effect on the discretization outcome.
• Robustness to Skewness and Outliers: Unlike parametric approaches, Khiops does not rely on moments (mean, variance, skewness) or density estimation, making it naturally resilient to skewed distributions and outliers.
• No Need for Normalization or Standardization: Since Khiops operates on ranks rather than raw values, common preprocessing steps like Z-score normalization (subtracting the mean and dividing by the standard deviation) or standardization to 0-1 range, are unnecessary. Feature scaling does not impact the learning process.

Conclusion

Transformations aimed at normalizing distributions or correcting skewness are unnecessary in Khiops. Its MODL-based encoding automatically adapts to the data, ensuring optimal variable encoding without manual intervention.