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About

Coverage.py is a tool for measuring code coverage of Python programs. It monitors your program, noting which parts of the code have been executed, then analyzes the source to identify code that could have been executed but was not. Coverage measurement is typically used to gauge the effectiveness of tests. It can show which parts of your code are being exercised by tests, and which are not. Use coverage run to run your test suite and gather data. However you normally run your test suite, and you can run your test runner under coverage. If your test runner command starts with “python”, just replace the initial “python” with “coverage run”. To limit coverage measurement to code in the current directory, and also find files that weren’t executed at all, add the source argument to your coverage command line. By default, it will measure line (statement) coverage. It can also measure branch coverage. It can tell you what tests ran which lines.

About

Scikit-learn provides simple and efficient tools for predictive data analysis. Scikit-learn is a robust, open source machine learning library for the Python programming language, designed to provide simple and efficient tools for data analysis and modeling. Built on the foundations of popular scientific libraries like NumPy, SciPy, and Matplotlib, scikit-learn offers a wide range of supervised and unsupervised learning algorithms, making it an essential toolkit for data scientists, machine learning engineers, and researchers. The library is organized into a consistent and flexible framework, where various components can be combined and customized to suit specific needs. This modularity makes it easy for users to build complex pipelines, automate repetitive tasks, and integrate scikit-learn into larger machine-learning workflows. Additionally, the library’s emphasis on interoperability ensures that it works seamlessly with other Python libraries, facilitating smooth data processing.

Platforms Supported

Windows Supported
Mac Supported
Linux Supported
Cloud Not Supported
On-Premises Not Supported
iPhone Not Supported
iPad Not Supported
Android Not Supported
Chromebook Not Supported

Platforms Supported

Windows Supported
Mac Supported
Linux Supported
Cloud Not Supported
On-Premises Not Supported
iPhone Not Supported
iPad Not Supported
Android Not Supported
Chromebook Not Supported

Audience

Any user looking for a solution to measure line and branch coverage to produce test reports

Audience

Engineers and data scientists requiring a solution to manage and improve their machine learning research

Support

Phone Support Not Supported
24/7 Live Support Not Supported
Online Supported

Support

Phone Support Not Supported
24/7 Live Support Not Supported
Online Supported

API

Offers API Supported

API

Offers API Supported

Screenshots and Videos

Screenshots and Videos

Pricing

Free
Free Version Supported
Free Trial Not Supported

Pricing

Free
Free Version Supported
Free Trial Not Supported

Reviews/Ratings

Overall 0.0 / 5
ease 0.0 / 5
features 0.0 / 5
design 0.0 / 5
support 0.0 / 5

This software hasn't been reviewed yet. Be the first to provide a review:

Review this Software

Reviews/Ratings

Overall 0.0 / 5
ease 0.0 / 5
features 0.0 / 5
design 0.0 / 5
support 0.0 / 5

This software hasn't been reviewed yet. Be the first to provide a review:

Review this Software

Training

Documentation Supported
Webinars Not Supported
Live Online Not Supported
In Person Not Supported

Training

Documentation Supported
Webinars Not Supported
Live Online Not Supported
In Person Not Supported

Company Information

Coverage.py
United States
coverage.readthedocs.io/en/7.0.0/

Company Information

scikit-learn
United States
scikit-learn.org/stable/

Alternatives

Alternatives

Gensim

Gensim

Radim Řehůřek
blanket.js

blanket.js

Blanket.js
ML.NET

ML.NET

Microsoft
JCov

JCov

OpenJDK
MLlib

MLlib

Apache Software Foundation
Devel::Cover

Devel::Cover

metacpan
Keepsake

Keepsake

Replicate

Categories

Code Coverage Supported

Categories

Machine Learning Supported

Integrations

Python Supported
C Supported
Codecov Supported
DagsHub Not Supported
Databricks Not Supported
Django Supported
Flower Not Supported
GLM-5.2 Not Supported
GLM-5.3 Not Supported
HTML Supported
JSON Supported
MLJAR Studio Not Supported
Mako Supported
ModelOp Not Supported
Thunder Compute Not Supported
Tidelift Supported
Train in Data Not Supported
XML Supported
pytest Supported
pytest-cov Supported

Integrations

Python Supported
C Not Supported
Codecov Not Supported
DagsHub Supported
Databricks Supported
Django Not Supported
Flower Supported
GLM-5.2 Supported
GLM-5.3 Supported
HTML Not Supported
JSON Not Supported
MLJAR Studio Supported
Mako Not Supported
ModelOp Supported
Thunder Compute Supported
Tidelift Not Supported
Train in Data Supported
XML Not Supported
pytest Not Supported
pytest-cov Not Supported
Claim Coverage.py and update features and information
Claim Coverage.py and update features and information
Claim scikit-learn and update features and information
Claim scikit-learn and update features and information