ml_logistic_regression 0.0.1 
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A lightweight, stable and trainable Logistic Regression package in pure Dart. Supports sigmoid prediction, L2 regularization, and classification thresholds.
We analyzed this package 3 days ago, and awarded it 130 pub points (of a possible 160):
10/10 points: Provide a valid pubspec.yaml
Issue tracker URL doesn't exist.
At the time of the analysis https://github.com/CelkMehmett/ml_logistic_regression/issues was unreachable. Make sure that the website is reachable via HEAD requests.
0/5 points: Provide a valid README.md
`README.md` is empty.
The README.md file should inform others about your project, what it does, and how they can use it. Check out the guidelines on Writing great package pages.
0/5 points: Provide a valid CHANGELOG.md
`CHANGELOG.md` is empty.
Changelog entries help developers follow the progress of your package. Check out the Dart conventions for Maintaining a package changelog.
10/10 points: Use an OSI-approved license
Detected license: MIT.
0/10 points: 20% or more of the public API has dartdoc comments
1 out of 13 API elements (7.7 %) have documentation comments.
Providing good documentation for libraries, classes, functions, and other API elements improves code readability and helps developers find and use your API. Document at least 20% of the public API elements.
To highlight public API members missing documentation consider enabling the public_member_api_docs lint.
Some symbols that are missing documentation: ml_logistic_regression, ml_logistic_regression.LogisticRegression.LogisticRegression.new, ml_logistic_regression.LogisticRegression.fit, ml_logistic_regression.LogisticRegression.iterations, ml_logistic_regression.LogisticRegression.learningRate.
10/10 points: Package has an example
20/20 points: Supports 6 of 6 possible platforms (iOS, Android, Web, Windows, macOS, Linux)
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✓ Android
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✓ iOS
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✓ Windows
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✓ Linux
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✓ macOS
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✓ Web
0/0 points: WASM compatibility
This package is compatible with runtime wasm, and will be rewarded additional points in a future version of the scoring model.
See https://dart.dev/web/wasm for details.
40/50 points: code has no errors, warnings, lints, or formatting issues
/tmp/pana_WLDPVE/lib/ml_logistic_regression.dart doesn't match the Dart formatter.
To format your files run: dart format .
10/10 points: All of the package dependencies are supported in the latest version
No dependencies.
To reproduce run dart pub outdated --no-dev-dependencies --up-to-date --no-dependency-overrides.
10/10 points: Package supports latest stable Dart and Flutter SDKs
20/20 points: Compatible with dependency constraint lower bounds
pub downgrade does not expose any static analysis error.
Analyzed with Pana 0.22.21, Dart 3.8.1.
Check the analysis log for details.
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Publisher
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Metadata
A lightweight, stable and trainable Logistic Regression package in pure Dart. Supports sigmoid prediction, L2 regularization, and classification thresholds.
Repository (GitHub)
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Topics
#dart #machine-learning #logistic-regression #ai #classification
Documentation
License
MIT (license)