ml_metrics 0.1.0 
ml_metrics: ^0.1.0 copied to clipboard
A lightweight and easy-to-use Dart library for evaluating machine learning models. Includes accuracy, precision, recall, F1 score, and confusion matrix for binary classification.
We analyzed this package 6 days ago, and awarded it 140 pub points (of a possible 160):
10/10 points: Provide a valid pubspec.yaml
5/5 points: Provide a valid README.md
5/5 points: Provide a valid CHANGELOG.md
10/10 points: Use an OSI-approved license
Detected license: MIT.
10/10 points: 20% or more of the public API has dartdoc comments
5 out of 12 API elements (41.7 %) have documentation comments.
Some symbols that are missing documentation: accuracy, confusion_matrix, f1_score, metric_utils, ml_metrics.
0/10 points: Package has an example
No example found.
See package layout guidelines on how to add 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
WASM-ready: This package is compatible with runtime wasm. See https://dart.dev/web/wasm for details.
40/50 points: code has no errors, warnings, lints, or formatting issues
Found 8 issues. Showing the first 2:
INFO: Statements in an if should be enclosed in a block.
lib/metrics/confusion_matrix.dart:15:39
╷
15 │ if (predLabel == positiveLabel) tp++;
│ ^^^^^
╵
To reproduce make sure you are using the lints_core and run dart analyze lib/metrics/confusion_matrix.dart
INFO: Statements in an if should be enclosed in a block.
lib/metrics/confusion_matrix.dart:16:12
╷
16 │ else fn++;
│ ^^^^^
╵
To reproduce make sure you are using the lints_core and run dart analyze lib/metrics/confusion_matrix.dart
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To reproduce run dart pub outdated --no-dev-dependencies --up-to-date --no-dependency-overrides.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 0.23.10, Dart 3.11.1.
Check the analysis log for details.
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Metadata
Publisher
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Weekly Downloads
Metadata
A lightweight and easy-to-use Dart library for evaluating machine learning models. Includes accuracy, precision, recall, F1 score, and confusion matrix for binary classification.
Repository (GitHub)
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Topics
#machine-learning #metrics #evaluation #classification #dart
Documentation
License
MIT (license)
