ALPBench: A Benchmark for Active Learning Pipelines on Tabular Data

🌈 Abstract

The paper proposes ALPBench, a comprehensive benchmark for active learning pipelines that combines different learning algorithms and query strategies for tabular data classification tasks. ALPBench facilitates the specification, execution, and performance monitoring of active learning pipelines, ensuring reproducible evaluations. The benchmark includes 86 real-world tabular classification datasets and 5 active learning settings, yielding 430 active learning problems. The authors demonstrate the usefulness of ALPBench by evaluating 72 different active learning pipelines, finding that strong pipelines often combine state-of-the-art classification methods with information-based query strategies, while the choice of learning algorithm is the most crucial factor.

🙋 Q&A

[01] Contributions

1. What are the three main contributions of the paper? The paper's main contributions are:

• Proposing ALPBench, the first active learning benchmark for tabular data that allows combining different learning algorithms and query strategies into active learning pipelines.
• Providing an open-source implementation of ALPBench as an extensible Python package for applying and benchmarking active learning pipelines.
• Conducting an experimental study that showcases the usefulness of ALPBench by evaluating 72 different active learning pipelines on 86 real-world classification datasets across 2 settings, finding that strong pipelines often combine state-of-the-art classification methods with information-based query strategies.

2. What does the paper find regarding the importance of the learning algorithm choice compared to the query strategy choice? The paper finds that the choice of the learning algorithm is the most crucial factor, while the suitability of query strategies also varies for different datasets. This underlines the need to consider different learning algorithms in future empirical studies on active learning.

[02] Related Work

1. What are the limitations of existing active learning benchmarks identified in the paper? The paper identifies the following limitations of existing active learning benchmarks:

• They are often limited in the number of datasets considered, focusing only on binary classification datasets or using outdated query strategies.
• They typically fix a single learning algorithm, such as a deep neural network or an SVM, and recommend suitable query strategies for this choice, which might lead to biased results.
• They do not properly represent state-of-the-art methods for tabular data, such as gradient-boosted decision trees (GBDTs) and deep learning architectures.

2. How does the paper's benchmark, ALPBench, address these limitations? ALPBench addresses these limitations by:

• Providing a comprehensive benchmark with 86 real-world tabular classification datasets and 5 different active learning problem settings.
• Allowing the combination of a variety of learning algorithms, including state-of-the-art methods for tabular data, with different query strategies.
• Enabling the investigation of the interplay between learning algorithms and query strategies, rather than focusing on a single learning algorithm.

[03] Active Learning Pipelines

1. What are the three main components of an active learning pipeline (ALP) in ALPBench? The three main components of an ALP in ALPBench are:

1. A learning algorithm that implements the scikit-learn classifier interface and is responsible for model induction.
2. A query strategy that selects unlabeled data points to be queried by the oracle.
3. An optional initializer that can be used to select an initial set of data points.

2. How does ALPBench facilitate the specification and execution of active learning pipelines? ALPBench provides a modular design with simple interfaces for the individual components of an ALP, allowing for easy composition of different learning algorithms and query strategies into active learning pipelines. It also includes functionality for applying the composed pipelines to different datasets and experiment setups, ensuring reproducibility through logging and storing of relevant metadata.

[04] Experiments

1. What are the key findings from the experimental evaluation presented in the paper? The key findings from the experimental evaluation are:

• Strong active learning pipelines often combine state-of-the-art classification methods, such as TabPFN, Catboost, or Random Forest, as learning algorithms and information-based query strategies.
• The choice of the learning algorithm is the most crucial factor, while the suitability of query strategies also varies for different datasets.
• There are datasets and settings where active learning can lead to a decrease in performance, highlighting the need for further understanding of the interplay between learning algorithms, query strategies, and dataset characteristics.

2. How does the experimental scope of this paper compare to previous studies on active learning for tabular data? Compared to previous studies, the experimental evaluation in this paper is the most comprehensive, considering a larger number of datasets, learning algorithms, and query strategies. The paper also investigates different active learning settings, unlike previous studies that often focused on a single setting.

[05] Limitations and Broader Impact

1. What are the key limitations of the work discussed in the paper? The key limitations discussed in the paper are:

• The benchmark and evaluation study are limited to tabular classification problems and consider a specific set of active learning settings.
• The empirical study restricted the training time to 180 seconds per iteration, which might limit the generalizability for the large settings.

2. How does the paper discuss the potential societal impacts of the work? The paper states that, as the work aims to advance the field of machine learning, there are many potential societal consequences, but none that need to be specifically highlighted. The paper follows the NeurIPS Code of Ethics and does not identify any harms caused by the research process.