Choosing the Right Regularizer for Applied ML: Simulation Benchmarks of Popular Scikit-learn Regularization Frameworks
arXiv:2604.03541v1 Announce Type: new Abstract: This study surveys the historical development of regularization, tracing its evolution from stepwise regression in the 1960s to recent advancements in formal error control, structured penalties for non-independent features, Bayesian methods, and l0-based regularization (among other techniques). We empirically evaluate the performance of four canonical frameworks -- Ridge, Lasso, ElasticNet, and Post-Lasso OLS -- across 134,400 simulations spanning a 7-dimensional manifold grounded in eight production-grade machine learning models. Our findings demonstrate that for prediction accuracy when the sample-to-feature ratio is sufficient (n/p >= 78), Ridge, Lasso, and ElasticNet are nearly interchangeable. However, we find that Lasso recall is highly fragile under multicollinearity; at high condition numbers (kappa) and low SNR, Lasso recall collapses to 0.18 while ElasticNet maintains 0.93. Consequently, we advise practitioners against using La
arXiv:2604.03541v1 Announce Type: new Abstract: This study surveys the historical development of regularization, tracing its evolution from stepwise regression in the 1960s to recent advancements in formal error control, structured penalties for non-independent features, Bayesian methods, and l0-based regularization (among other techniques). We empirically evaluate the performance of four canonical frameworks -- Ridge, Lasso, ElasticNet, and Post-Lasso OLS -- across 134,400 simulations spanning a 7-dimensional manifold grounded in eight production-grade machine learning models. Our findings demonstrate that for prediction accuracy when the sample-to-feature ratio is sufficient (n/p >= 78), Ridge, Lasso, and ElasticNet are nearly interchangeable. However, we find that Lasso recall is highly fragile under multicollinearity; at high condition numbers (kappa) and low SNR, Lasso recall collapses to 0.18 while ElasticNet maintains 0.93. Consequently, we advise practitioners against using Lasso or Post-Lasso OLS at high kappa with small sample sizes. The analysis concludes with an objective-driven decision guide to assist machine learning engineers in selecting the optimal scikit-learn-supported framework based on observable feature space attributes.
Executive Summary
This study evaluates the performance of four regularization frameworks - Ridge, Lasso, ElasticNet, and Post-Lasso OLS - across 134,400 simulations. The authors find that Ridge, Lasso, and ElasticNet are nearly interchangeable for prediction accuracy when the sample-to-feature ratio is sufficient. However, Lasso recall is highly fragile under multicollinearity, collapsing to 0.18 at high condition numbers and low SNR. The authors advise against using Lasso or Post-Lasso OLS at high kappa with small sample sizes. The study provides an objective-driven decision guide to assist machine learning engineers in selecting the optimal framework based on feature space attributes. The findings have significant implications for practitioners in machine learning, highlighting the importance of careful selection of regularization techniques to avoid degraded performance.
Key Points
- ▸ The study evaluates the performance of four regularization frameworks across 134,400 simulations
- ▸ Ridge, Lasso, and ElasticNet are nearly interchangeable for prediction accuracy when the sample-to-feature ratio is sufficient
- ▸ Lasso recall is highly fragile under multicollinearity
- ▸ The authors provide an objective-driven decision guide for selecting the optimal framework
Merits
Comprehensive evaluation
The study conducts a thorough evaluation of four regularization frameworks across a large number of simulations, providing a comprehensive understanding of their performance.
Objective-driven decision guide
The authors provide a practical decision guide to assist machine learning engineers in selecting the optimal framework based on feature space attributes.
Demerits
Limited scope
The study focuses on a specific set of regularization frameworks and does not consider other techniques that may be relevant in certain contexts.
Assumes sufficient sample-to-feature ratio
The study's findings assume a sufficient sample-to-feature ratio, which may not be representative of all real-world scenarios.
Expert Commentary
This study provides a thorough evaluation of the performance of four regularization frameworks, which is a critical aspect of machine learning model development. The authors' findings highlight the importance of careful selection of regularization techniques to avoid degraded performance. However, the study assumes a sufficient sample-to-feature ratio, which may not be representative of all real-world scenarios. Additionally, the study focuses on a specific set of regularization frameworks, which may limit its generalizability. Nevertheless, the study's objective-driven decision guide provides a practical tool for machine learning engineers to select the optimal framework based on feature space attributes.
Recommendations
- ✓ Practitioners should carefully evaluate the performance of regularization techniques in their specific context before selecting a framework.
- ✓ Machine learning engineers should consider the sample-to-feature ratio and multicollinearity when selecting regularization techniques.
Sources
Original: arXiv - cs.LG