# Automatic Componentwise Boosting: An Interpretable AutoML System

arXiv: https://arxiv.org/abs/2109.05583 · HTML: https://mlsanity.com/paper/2109.05583/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 10 of its 34 references that are in the map.
- First posted on arXiv 12 Sep 2021.

## Builds on (most cited first, 10 of 10)

- [Hyperband: A Novel Bandit-Based Approach to Hyperparameter Optimization](https://mlsanity.com/paper/1603.06560/) (2016)
- [AutoGluon-Tabular: Robust and Accurate AutoML for Structured Data](https://mlsanity.com/paper/2003.06505/) (2020)
- [Non-stochastic Best Arm Identification and Hyperparameter Optimization](https://mlsanity.com/paper/1502.07943/) (2015)
- [An Open Source AutoML Benchmark](https://mlsanity.com/paper/1907.00909/) (2019)
- [Auto-PyTorch Tabular: Multi-Fidelity MetaLearning for Efficient and Robust AutoDL](https://mlsanity.com/paper/2006.13799/) (2020)
- [Trust in AutoML: Exploring Information Needs for Establishing Trust in Automated Machine Learning Systems](https://mlsanity.com/paper/2001.06509/) (2020)
- [Probing for sparse and fast variable selection with model-based boosting](https://mlsanity.com/paper/1702.04561/) (2017)
- [Automatic Gradient Boosting](https://mlsanity.com/paper/1807.03873/) (2018)
- [Boosting Factor-Specific Functional Historical Models for the Detection of Synchronisation in Bioelectrical Signals](https://mlsanity.com/paper/1609.06070/) (2016)
- [Towards Human Centered AutoML](https://mlsanity.com/paper/1911.02391/) (2019)

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

- [Improving Accuracy of Interpretability Measures in Hyperparameter Optimization via Bayesian Algorithm Execution](https://mlsanity.com/paper/2206.05447/) (2022)

## Abstract (arXiv)

In practice, machine learning (ML) workflows require various different steps, from data preprocessing, missing value imputation, model selection, to model tuning as well as model evaluation. Many of these steps rely on human ML experts. AutoML - the field of automating these ML pipelines - tries to help practitioners to apply ML off-the-shelf without any expert knowledge. Most modern AutoML systems like auto-sklearn, H20-AutoML or TPOT aim for high predictive performance, thereby generating ensembles that consist almost exclusively of black-box models. This, in turn, makes the interpretation for the layperson more intricate and adds another layer of opacity for users. We propose an AutoML system that constructs an interpretable additive model that can be fitted using a highly scalable componentwise boosting algorithm. Our system provides tools for easy model interpretation such as visualizing partial effects and pairwise interactions, allows for a straightforward calculation of feature importance, and gives insights into the required model complexity to fit the given task. We introduce the general framework and outline its implementation autocompboost. To demonstrate the frameworks efficacy, we compare autocompboost to other existing systems based on the OpenML AutoML-Benchmark. Despite its restriction to an interpretable model space, our system is competitive in terms of predictive performance on most data sets while being more user-friendly and transparent.