📊 ml_metrics #

A lightweight and efficient Dart library for evaluating machine learning models.
Supports binary classification metrics such as Accuracy, Precision, Recall, F1 Score, and Confusion Matrix.

✨ Features #

• ✅ Accuracy score
• ✅ Precision & Recall (binary classification)
• ✅ F1 Score
• ✅ Confusion Matrix
• ✅ Fully tested & null-safe

📦 Installation #

Add the following to your pubspec.yaml:

dependencies:
  ml_metrics: ^0.1.0

Then run:

dart pub get

🚀 Quick Example #

import 'package:ml_metrics/ml_metrics.dart';

void main() {
  final yTrue = [1, 0, 1, 1, 0];
  final yPred = [1, 0, 0, 1, 1];

  print('Accuracy: ${accuracy(yTrue, yPred)}');
  print('Precision: ${precision(yTrue, yPred)}');
  print('Recall: ${recall(yTrue, yPred)}');
  print('F1 Score: ${f1Score(yTrue, yPred)}');
  print('Confusion Matrix: ${binaryConfusionMatrix(yTrue, yPred)}');
}

📚 Metrics Reference #

🧪 Run Tests #

dart test

🔗 Links #

• GitHub Repository
• Pub.dev Package

📄 License #

This project is licensed under the MIT License. See the LICENSE file for details.