Summary of Generalized Resubstitution For Regression Error Estimation, by Diego Marcondes and Ulisses Braga-neto
Generalized Resubstitution for Regression Error Estimation
by Diego Marcondes, Ulisses Braga-Neto
First submitted to arxiv on: 23 Oct 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: None
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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 The proposed generalized resubstitution error estimators for regression offer a broad family of methods, each corresponding to a choice of empirical probability measures and loss functions. This includes the standard sum of squares criterion as a special case. By selecting alternative empirical probability measures, more general estimators with improved bias and variance properties can be obtained. The consistency of these estimators under broad assumptions is proven. Furthermore, procedures for choosing the empirical measure based on the method of moments and maximum pseudo-likelihood are introduced and evaluated using polynomial regression experiments. Experimental results demonstrate the superior finite-sample bias and variance properties of the proposed estimators. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary We’re going to talk about a new way to estimate errors in regression models. Regression is a type of math that helps us understand how things relate to each other. The error estimator is like a tool that tells us how good our model is at predicting what will happen. Right now, there are standard ways to do this, but these new estimators offer some improvements. They can be used in different ways depending on the situation, and they work well even with limited data. This makes them useful for real-world applications. |
Keywords
» Artificial intelligence » Likelihood » Probability » Regression
