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[R] Show Your Work with Confidence: Confidence Bands for Tuning Curves
April 18, 2024, 4:46 p.m. | /u/nicholaslourie
Machine Learning www.reddit.com
Tweet: [https://x.com/NickLourie/status/1770077925779337563](https://x.com/NickLourie/status/1770077925779337563)
Code: [https://github.com/nicholaslourie/opda](https://github.com/nicholaslourie/opda)
Docs: [https://nicholaslourie.github.io/opda/tutorial/usage.html](https://nicholaslourie.github.io/opda/tutorial/usage.html)
Abstract:
>The choice of hyperparameters greatly impacts performance in natural language processing. Often, it is hard to tell if a method is better than another or just better tuned. Tuning curves fix this ambiguity by accounting for tuning effort. Specifically, they plot validation performance as a function of the number of hyperparameter choices tried so far. While several estimators exist for these curves, it is common to use point estimates, which we …
abstract accounting function hyperparameter impacts language language processing machinelearning natural natural language natural language processing performance plot processing validation
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