Generative and reproducible benchmarks for comprehensive evaluation of machine learning classifiers
This provides a standardized tool for researchers and practitioners to assess classifier performance, but it is incremental as it builds on existing benchmarking approaches.
The authors tackled the problem of evaluating machine learning classifiers by introducing DIGEN, a collection of 40 synthetic datasets generated from mathematical functions, which provides a reproducible and interpretable benchmark for comparing algorithms, though no concrete performance numbers are reported.
Understanding the strengths and weaknesses of machine learning (ML) algorithms is crucial for determine their scope of application. Here, we introduce the DIverse and GENerative ML Benchmark (DIGEN) - a collection of synthetic datasets for comprehensive, reproducible, and interpretable benchmarking of machine learning algorithms for classification of binary outcomes. The DIGEN resource consists of 40 mathematical functions which map continuous features to discrete endpoints for creating synthetic datasets. These 40 functions were discovered using a heuristic algorithm designed to maximize the diversity of performance among multiple popular machine learning algorithms thus providing a useful test suite for evaluating and comparing new methods. Access to the generative functions facilitates understanding of why a method performs poorly compared to other algorithms thus providing ideas for improvement. The resource with extensive documentation and analyses is open-source and available on GitHub.