Metrics

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Metrics is a Coding & Development tool that provides implementations of various supervised machine learning evaluation metrics. It supports multiple languages including Python, R, Haskell, and MATLAB/Octave.

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At a glance

Pricing

Open Source

Free tier

Yes

API

Skill level

Technical

About

What is Metrics?

Metrics is an open-source toolbox offering implementations of various supervised machine learning evaluation metrics across multiple programming languages. Developers and researchers can utilize this tool to assess model performance in Python, R, Haskell, and MATLAB/Octave environments. It includes a wide array of metrics such as Absolute Error, Area Under the ROC, F1 Score, Log Loss, Mean Absolute Error, Mean Squared Error, and Root Mean Squared Error. The project is currently in a beta release, focusing on ensuring compatibility and functionality across its supported language repositories. It aims to provide a comprehensive suite for evaluating machine learning models.

Best used for

Ideal for developers who need to evaluate the performance of supervised machine learning models, compare different algorithms, and integrate evaluation metrics into their projects. Especially valuable for those working in multi-language environments like Python, R, Haskell, or MATLAB/Octave.

Common actions

evaluate machine learning models

compare model performance

implement evaluation metrics

analyze model accuracy

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Capabilities

Key features

Multi-language support
Supervised ML evaluation metrics
Absolute Error (AE)
Area Under ROC (AUC)
F1 Score (F1)
Mean Absolute Error (MAE)
Mean Squared Error (MSE)

Target Audience

developer

Integrations

Not yet documented

Pricing & Plans

Open Source

Free

FAQs

What programming languages does Metrics support for evaluation?

Metrics provides implementations for machine learning evaluation metrics in Python, R, Haskell, and MATLAB/Octave. This allows users to apply the same evaluation standards across different development environments and projects, catering to a broad range of technical users.

How can I install Metrics in my preferred programming environment?

Installation varies by language: Python users can use `easy_install ml_metrics`, R users can `install.packages("Metrics")`, and Haskell users can `cabal install Metrics`. For MATLAB/Octave, you need to clone the repository and run the setup from the MATLAB command line.

What types of evaluation metrics are included in the Metrics toolbox?

The toolbox includes a comprehensive set of supervised machine learning evaluation metrics such as Absolute Error (AE), Area Under the ROC (AUC), F1 Score, Log Loss, Mean Absolute Error (MAE), Mean Squared Error (MSE), and Root Mean Squared Error (RMSE), among others.

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Code Assistants DevOps & Infrastructure No-Code / Low-Code Testing & QA Backend & APIs Prompt Engineering

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Also listed in

This tool also appears in

Research & Education › Scientific Computing Data & Analytics › Statistical & Scientific

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