Summary of Prevalidated Ridge Regression Is a Highly-efficient Drop-in Replacement For Logistic Regression For High-dimensional Data, by Angus Dempster et al.
Prevalidated ridge regression is a highly-efficient drop-in replacement for logistic regression for high-dimensional data
by Angus Dempster, Geoffrey I. Webb, Daniel F. Schmidt
First submitted to arxiv on: 28 Jan 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Machine Learning (stat.ML)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary Logistic regression is a widely used technique for probabilistic classification, but its effectiveness relies on careful tuning, particularly for the regularization hyperparameter. In high-dimensional data, this process becomes computationally expensive and challenging. To address this issue, we propose a pre-validated ridge regression model that closely matches logistic regression in terms of classification error and log-loss, especially in high-dimensional settings. Our approach is more efficient and has fewer hyperparameters to tune, making it a promising alternative. By scaling the coefficients of our model using leave-one-out cross-validation error, we minimize log-loss with nominal additional computational expense. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Logistic regression is a popular way to predict probabilities. However, making it work well requires careful adjustments, which can be time-consuming and difficult, especially when dealing with large amounts of data. We created a new method called ridge regression that achieves similar results to logistic regression but in a more efficient and easier-to-use way. Our approach also has fewer settings to adjust, making it simpler to use. By using pre-computed information, we can quickly find the best combination of coefficients for our model without adding much extra work. |
Keywords
* Artificial intelligence * Classification * Hyperparameter * Logistic regression * Regression * Regularization
