marcelovca90/auto-ml-evaluation
Code of the article "A practical evaluation of AutoML tools for binary, multiclass, and multilabel classification".
When you need to choose the best automated machine learning (AutoML) tool for your classification problem, this project helps by comparing 16 popular tools across different types of classification tasks (binary, multiclass, multilabel) and 21 real-world datasets. It provides a comprehensive evaluation of how each tool performs in terms of accuracy and speed. Data scientists and machine learning engineers can use the results to inform their tool selection for specific projects.
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Use this if you are a data scientist or ML engineer struggling to choose the most effective AutoML tool for a classification task and need objective performance benchmarks.
Not ideal if you are looking for an AutoML tool to directly apply to your dataset, rather than an evaluation of existing tools.
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MIT
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Last pushed
May 15, 2025
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