# The Potential Benefits of Filtering Versus Hyper-Parameter Optimization

arXiv: https://arxiv.org/abs/1403.3342 · HTML: https://mlsanity.com/paper/1403.3342/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 13 Mar 2014.

## Built on by (most cited first, 1 of 1)

- [Filtering out mislabeled training instances using black-box optimization and quantum annealing](https://mlsanity.com/paper/2501.06916/) (2025)

## Abstract (arXiv)

The quality of an induced model by a learning algorithm is dependent on the quality of the training data and the hyper-parameters supplied to the learning algorithm. Prior work has shown that improving the quality of the training data (i.e., by removing low quality instances) or tuning the learning algorithm hyper-parameters can significantly improve the quality of an induced model. A comparison of the two methods is lacking though. In this paper, we estimate and compare the potential benefits of filtering and hyper-parameter optimization. Estimating the potential benefit gives an overly optimistic estimate but also empirically demonstrates an approximation of the maximum potential benefit of each method. We find that, while both significantly improve the induced model, improving the quality of the training set has a greater potential effect than hyper-parameter optimization.