# AVATAR -- Machine Learning Pipeline Evaluation Using Surrogate Model

arXiv: https://arxiv.org/abs/2001.11158 · HTML: https://mlsanity.com/paper/2001.11158/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 30 Jan 2020.

## Built on by (most cited first, 5 of 5)

- [Incremental Search Space Construction for Machine Learning Pipeline Synthesis](https://mlsanity.com/paper/2101.10951/) (2021)
- [Exploring Opportunistic Meta-knowledge to Reduce Search Spaces for Automated Machine Learning](https://mlsanity.com/paper/2105.00282/) (2021)
- [Generating Skyline Datasets for Data Science Models](https://mlsanity.com/paper/2502.11262/) (2025)
- [Designing Machine Learning Pipeline Toolkit for AutoML Surrogate Modeling Optimization](https://mlsanity.com/paper/2107.01253/) (2021)
- [On Taking Advantage of Opportunistic Meta-knowledge to Reduce Configuration Spaces for Automated Machine Learning](https://mlsanity.com/paper/2208.04376/) (2022)

## Abstract (arXiv)

The evaluation of machine learning (ML) pipelines is essential during automatic ML pipeline composition and optimisation. The previous methods such as Bayesian-based and genetic-based optimisation, which are implemented in Auto-Weka, Auto-sklearn and TPOT, evaluate pipelines by executing them. Therefore, the pipeline composition and optimisation of these methods requires a tremendous amount of time that prevents them from exploring complex pipelines to find better predictive models. To further explore this research challenge, we have conducted experiments showing that many of the generated pipelines are invalid, and it is unnecessary to execute them to find out whether they are good pipelines. To address this issue, we propose a novel method to evaluate the validity of ML pipelines using a surrogate model (AVATAR). The AVATAR enables to accelerate automatic ML pipeline composition and optimisation by quickly ignoring invalid pipelines. Our experiments show that the AVATAR is more efficient in evaluating complex pipelines in comparison with the traditional evaluation approaches requiring their execution.