# Mapping Learning Algorithms on Data, a useful step for optimizing performances and their comparison

arXiv: https://arxiv.org/abs/2107.06981 · HTML: https://mlsanity.com/paper/2107.06981/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 2 of its 49 references that are in the map.
- First posted on arXiv 14 Jul 2021.

## Builds on (most cited first, 2 of 2)

- [How to Identify Investor's types in real financial markets by means of agent based simulation](https://mlsanity.com/paper/2101.03127/) (2020)
- [Domain Specific Concept Drift Detectors for Predicting Financial Time Series](https://mlsanity.com/paper/2103.14079/) (2021)

## Abstract (arXiv)

In the paper, we propose a novel methodology to map learning algorithms on data (performance map) in order to gain more insights in the distribution of their performances across their parameter space. This methodology provides useful information when selecting a learner's best configuration for the data at hand, and it also enhances the comparison of learners across learning contexts. In order to explain the proposed methodology, the study introduces the notions of learning context, performance map, and high performance function. It then applies these concepts to a variety of learning contexts to show how their use can provide more insights in a learner's behavior, and can enhance the comparison of learners across learning contexts. The study is completed by an extensive experimental study describing how the proposed methodology can be applied.