MLme: Supporting Data for a Machine Learning Toolkit Evaluation

The paper describes a machine learning toolkit tested on six distinct datasets. The datasets were used to evaluate the MLme tool, which integrates Data Exploration, AutoML, CustomML, and Visualization functionalities. The supporting data originates from the University of Bern and accompanies an open-source toolkit for classification problems.

Use Cases

• Benchmarking AutoML tools based on the six distinct test datasets mentioned in the description
• Identifying cell population biomarkers based on the feature selection functionality demonstrated for CD8+, CD16+, and CD14+ cells
• Conducting data exploration for classification problems using the integrated Data Exploration module

Strengths

• Tool was rigorously tested on six distinct datasets, each with unique characteristics
• Demonstrated successful identification of specific biomarkers (BACH2, CD16, VCAN) for immune cell populations

Limitations

• Row count and dataset scale are unknown, which may limit suitability assessment
• Column-level documentation is absent; field semantics must be inferred after download
• Last update date is unknown; freshness unverified

Provenance

Source

University of Bern

Collection Method

Datasets used for testing a machine learning toolkit, likely from public or research sources.