Summary of Forecal: Random Forest-based Calibration For Dnns, by Dhruv Nigam
ForeCal: Random Forest-based Calibration for DNNs
by Dhruv Nigam
First submitted to arxiv on: 4 Sep 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 In this research paper, a novel post-hoc calibration algorithm called ForeCal is proposed to improve the accuracy and calibration of deep neural network-based classifiers. The existing methods like Isotonic regression, Platt scaling, and Temperature scaling have limitations in their parametric assumptions and inability to capture complex non-linear relationships. ForeCal leverages two unique properties of Random forests: enforcing weak monotonicity and range-preservation. It outperforms current state-of-the-art methods in terms of Expected Calibration Error (ECE) on 43 diverse datasets from the UCI ML repository, while maintaining the discriminative power of the base DNN as measured by AUC. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about a new way to make sure that deep learning models are giving accurate answers. The models are really good at picking out patterns in data, but sometimes they’re not very good at telling us how likely it is that something will happen. The researchers came up with a new method called ForeCal that helps fix this problem by using a type of machine learning algorithm called Random forests. This new method works well and can even use extra information to make its predictions more accurate. |
Keywords
» Artificial intelligence » Auc » Deep learning » Machine learning » Neural network » Regression » Temperature
