Metrics
A multi-language library providing implementations of common supervised machine learning evaluation metrics.
What is Metrics?
Metrics is a multi-language library that provides implementations of common supervised machine learning evaluation metrics. It solves the problem of inconsistent or error-prone metric calculations by offering standardized, reliable functions across Python, R, Haskell, and MATLAB/Octave. This ensures researchers and practitioners can accurately evaluate and compare models regardless of their programming environment.
Machine learning practitioners, data scientists, and researchers who need to evaluate supervised learning models across multiple programming languages or ensure metric calculation consistency.
Developers choose Metrics because it provides consistent, well-tested implementations of evaluation metrics across multiple languages, reducing calculation errors and enabling fair model comparisons in multi-language environments.
Overview
Machine learning evaluation metrics, implemented in Python, R, Haskell, and MATLAB / Octave
Use Cases
Best For
- Evaluating supervised machine learning models across different programming languages
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