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Related Experiment Video

Updated: Jan 10, 2026

The Innovation Arena: A Method for Comparing Innovative Problem-Solving Across Groups
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SplitWise regression for capturing nonlinear effects in interpretable model selection.

Marcell T Kurbucz1, Nikolaos Tzivanakis2, Nilufer Sari Aslam2

  • 1Institute for Global Prosperity, The Bartlett, University College London, 9-11 Endsleigh Gardens, London, WC1H 0EH, UK. m.kurbucz@ucl.ac.uk.

Scientific Reports
|November 27, 2025
PubMed
Summary
This summary is machine-generated.

SplitWise is a new regression framework that balances model interpretability with nonlinear relationships. It adaptively transforms predictors, improving accuracy while maintaining transparent, linear models.

Keywords:
Dummy variablesInterpretable modelingModel selectionSoftwareStepwise regressionThreshold effects

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Area of Science:

  • Statistics
  • Machine Learning
  • Data Science

Background:

  • Regression modeling often struggles to balance capturing nonlinear relationships with maintaining model interpretability.
  • Classical linear models offer transparency but cannot effectively model complex nonlinear patterns.
  • Existing interpretable nonlinear methods may lack flexibility or introduce complexity.

Purpose of the Study:

  • Introduce SplitWise, a novel stepwise regression framework designed to bridge the gap between linear interpretability and nonlinear flexibility.
  • To develop a method that adaptively transforms numeric predictors into binary features only when beneficial for model fit.
  • To provide a regression approach that remains interpretable and verifiable while capturing threshold-based nonlinear effects.

Main Methods:

  • SplitWise employs shallow decision trees to identify optimal thresholds for transforming numeric predictors into binary features.
  • Model fit is assessed using Akaike Information Criterion (AIC) or Bayesian Information Criterion (BIC) to guide predictor transformation.
  • The framework integrates these binary features into a globally linear equation, preserving overall model transparency.

Main Results:

  • On synthetic data, SplitWise reduced median Root Mean Squared Error (RMSE) by 7-14% compared to linear baselines.
  • Variable selection accuracy was improved, with Matthews Correlation Coefficient (MCC) up to ~0.79 versus ~0.51 for LASSO.
  • On real datasets (Wine Quality, Bodyfat), SplitWise achieved comparable or improved RMSE with fewer selected predictors than other methods.

Conclusions:

  • SplitWise offers an interpretable yet flexible approach to regression modeling, effectively capturing threshold-based nonlinearities.
  • The framework enhances predictive accuracy and variable selection compared to traditional linear models and some advanced methods.
  • SplitWise represents a valuable tool for researchers and practitioners seeking transparent models with enhanced predictive power.