Summary of A Provably Accurate Randomized Sampling Algorithm For Logistic Regression, by Agniva Chowdhury et al.
A Provably Accurate Randomized Sampling Algorithm for Logistic Regression
by Agniva Chowdhury, Pradeep Ramuhalli
First submitted to arxiv on: 26 Feb 2024
Categories
- Main: Machine Learning (stat.ML)
- Secondary: Data Structures and Algorithms (cs.DS); Machine Learning (cs.LG)
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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 fundamental machine learning technique used for binary classification tasks. In this paper, we present a simple algorithm that utilizes randomized sampling-based methods to achieve high-quality approximations of estimated probabilities and model discrepancies in logistic regression problems. Our approach relies on two structural conditions that involve randomized matrix multiplication, a well-established concept in randomized numerical linear algebra. We prove that accurate estimates can be obtained with a sample size much smaller than the total number of observations. To validate our findings, we conduct comprehensive empirical evaluations. This work highlights the potential of using randomized sampling approaches to efficiently approximate estimated probabilities in logistic regression, offering a practical and computationally efficient solution for large-scale datasets. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Logistic regression is a popular machine learning tool used to predict yes or no answers. In this study, scientists developed a new way to use random samples to quickly estimate the accuracy of these predictions. They showed that even with a very small sample size, their method can produce accurate results for large datasets. This breakthrough could make it easier and faster to analyze big data sets in fields like medicine, finance, and social sciences. |
Keywords
* Artificial intelligence * Classification * Logistic regression * Machine learning
