# Ridge Regularization: an Essential Concept in Data Science

arXiv: https://arxiv.org/abs/2006.00371 · HTML: https://mlsanity.com/paper/2006.00371/

## Key facts

- Cited by 19 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 30 May 2020.

## Built on by (most cited first, 19 of 19)

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- [Subsample Ridge Ensembles: Equivalences and Generalized Cross-Validation](https://mlsanity.com/paper/2304.13016/) (2023)
- [Generalized equivalences between subsampling and ridge regularization](https://mlsanity.com/paper/2305.18496/) (2023)
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- [Smooth Nested Simulation: Bridging Cubic and Square Root Convergence Rates in High Dimensions](https://mlsanity.com/paper/2201.02958/) (2022)
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- [Model-free Distortion Canceling and Control of Quantum Devices](https://mlsanity.com/paper/2407.09877/) (2024)
- [Helix 1.0: An Open-Source Framework for Reproducible and Interpretable Machine Learning on Tabular Scientific Data](https://mlsanity.com/paper/2507.17791/) (2025)
- [Finite-Sample Inference for Sparsely Permuted Linear Regression](https://mlsanity.com/paper/2601.14872/) (2026)
- [Statistical comparisons of time-series feature sets on classification tasks](https://mlsanity.com/paper/2608.01586/) (2026)
- [QuaSAR: Quantization Compensation via Stable Activation-Aware Rank Truncation](https://mlsanity.com/paper/2608.14149/) (2026)

## Abstract (arXiv)

Ridge or more formally ℓ2 regularization shows up in many areas of statistics and machine learning. It is one of those essential devices that any good data scientist needs to master for their craft. In this brief ridge fest I have collected together some of the magic and beauty of ridge that my colleagues and I have encountered over the past 40 years in applied statistics.