# Towards explainable meta-learning

arXiv: https://arxiv.org/abs/2002.04276 · HTML: https://mlsanity.com/paper/2002.04276/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 11 Feb 2020.

## Built on by (most cited first, 4 of 4)

- [Consolidated learning -- a domain-specific model-free optimization strategy with examples for XGBoost and MIMIC-IV](https://mlsanity.com/paper/2201.11815/) (2022)
- [MetaLLMix : An XAI Aided LLM-Meta-learning Based Approach for Hyper-parameters Optimization](https://mlsanity.com/paper/2509.09387/) (2025)
- [forester: A Tree-Based AutoML Tool in R](https://mlsanity.com/paper/2409.04789/) (2024)
- [TLXML: Task-Level Explanation of Meta-Learning via Influence Functions](https://mlsanity.com/paper/2501.14271/) (2025)

## Abstract (arXiv)

Meta-learning is a field that aims at discovering how different machine learning algorithms perform on a wide range of predictive tasks. Such knowledge speeds up the hyperparameter tuning or feature engineering. With the use of surrogate models various aspects of the predictive task such as meta-features, landmarker models e.t.c. are used to predict the expected performance. State of the art approaches are focused on searching for the best meta-model but do not explain how these different aspects contribute to its performance. However, to build a new generation of meta-models we need a deeper understanding of the importance and effect of meta-features on the model tunability. In this paper, we propose techniques developed for eXplainable Artificial Intelligence (XAI) to examine and extract knowledge from black-box surrogate models. To our knowledge, this is the first paper that shows how post-hoc explainability can be used to improve the meta-learning.