https://github.com/dr-saad-la/ml-evaluation-metrics
Machine Learning Evaluation Metrics
https://github.com/dr-saad-la/ml-evaluation-metrics
classification clustering deep-learning evaluation-metrics machine-learning regression
Last synced: 7 months ago
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Machine Learning Evaluation Metrics
- Host: GitHub
- URL: https://github.com/dr-saad-la/ml-evaluation-metrics
- Owner: dr-saad-la
- License: apache-2.0
- Created: 2025-01-06T21:15:51.000Z (9 months ago)
- Default Branch: main
- Last Pushed: 2025-01-07T04:53:09.000Z (9 months ago)
- Last Synced: 2025-01-28T18:19:55.076Z (9 months ago)
- Topics: classification, clustering, deep-learning, evaluation-metrics, machine-learning, regression
- Language: Python
- Homepage:
- Size: 195 KB
- Stars: 1
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- Contributing: CONTRIBUTING.md
- License: LICENSE
- Code of conduct: CODE_OF_CONDUCT.md
Awesome Lists containing this project
README
# Machine Learning Evaluation Metrics Guide
[](https://github.com/laouadi/ml-evaluation-metrics/stargazers)
[](https://github.com/laouadi/ml-evaluation-metrics/blob/main/LICENSE)
[](https://github.com/laouadi/ml-evaluation-metrics/issues)
[](https://github.com/laouadi/ml-evaluation-metrics/pulls)
## 📚 Overview
A comprehensive guide to evaluation metrics in machine learning, covering everything from basic classification metrics to advanced deep learning evaluation techniques. This repository serves as both a theoretical reference and practical implementation guide for ML practitioners, researchers, and students.
## 🎯 Categories
### 1. Classification Metrics
- F1 Score, Precision, Recall
- Cohen's Kappa
- Matthews Correlation Coefficient (MCC)
- Balanced Accuracy
- Specificity and Sensitivity
- DOR (Diagnostic Odds Ratio)
### 2. Regression Metrics
- RMSE (Root Mean Square Error)
- MAE (Mean Absolute Error)
- MAPE (Mean Absolute Percentage Error)
- R-squared and Adjusted R-squared
- SMAPE (Symmetric Mean Absolute Percentage Error)
- Huber Loss
### 3. Ranking Metrics
- NDCG (Normalized Discounted Cumulative Gain)
- MAP (Mean Average Precision)
- MRR (Mean Reciprocal Rank)
- AUC-PR (Area Under Precision-Recall Curve)
### 4. Clustering Metrics
- Silhouette Score
- Davies-Bouldin Index
- Calinski-Harabasz Index
- Dunn Index
- Rand Index
- Mutual Information Based Scores
### 5. Distance Metrics
- Euclidean Distance
- Manhattan Distance
- Cosine Similarity
- Jaccard Similarity
- Hamming Distance
- Mahalanobis Distance
### 6. Information Theory Metrics
- Cross-Entropy
- KL Divergence
- Mutual Information
- Information Gain
- Entropy Measures
### 7. Deep Learning Specific Metrics
- Inception Score
- FID (Fréchet Inception Distance)
- BLEU Score (for NLP)
- ROUGE Score (for text summarization)
- METEOR Score
- Perplexity
## 📋 Prerequisites
- Python 3.8+
- NumPy
- Pandas
- Scikit-learn
- TensorFlow/PyTorch (for deep learning metrics)
- Matplotlib/Seaborn (for visualizations)
## 🚀 Installation
### Using Poetry (recommended)
```bash
# Clone the repository
git clone https://github.com/dr-saad-la/ml-evaluation-metrics.git
cd ml-evaluation-metrics
# Install with Poetry
poetry install
```
### Using Pip
```bash
git clone https://github.com/dr-saad-laouadi/ml-evaluation-metrics.git
cd ml-evaluation-metrics
pip install -r requirements.txt
```
## 📖 Documentation Structure
Each metric in this repository is thoroughly documented with:
- 📐 **Mathematical Formulation**
- Detailed mathematical definitions
- Variable explanations
- Formula derivations when relevant
- 💡 **Implementation Details**
- Step-by-step implementation guide
- Best practices and optimization tips
- Common pitfalls and solutions
- 🎯 **Use Cases and Examples**
- Real-world applications
- Industry-specific scenarios
- Practical implementation examples
- ⚖️ **Advantages and Limitations**
- Strengths and weaknesses
- Performance considerations
- Comparison with similar metrics
- 📊 **Visual Explanations**
- Intuitive diagrams and plots
- Performance visualizations
- Comparative analysis charts
- 💻 **Code Examples**
- Ready-to-use implementations
- Unit tests
- Performance benchmarks
## 💻 Usage Examples
```python
# Import necessary metrics
from ml_metrics.classification import f1_score
from ml_metrics.regression import rmse
from ml_metrics.clustering import silhouette
# Classification metric example
y_true = [1, 0, 1, 1, 0]
y_pred = [1, 0, 0, 1, 0]
f1 = f1_score(y_true, y_pred)
# Regression metric example
y_true = [3.2, 4.5, 5.0, 1.2]
y_pred = [3.1, 4.3, 5.2, 1.1]
error = rmse(y_true, y_pred)
# Clustering metric example
labels = [0, 0, 1, 1]
features = [[1,2], [2,2], [8,9], [9,9]]
score = silhouette(features, labels)
```
## 🤝 Contributing
We warmly welcome contributions from the community! Here's how you can help:
1. Fork the Project
2. Create your Feature Branch (`git checkout -b feature/AmazingFeature`)
3. Commit your Changes (`git commit -m 'Add some AmazingFeature'`)
4. Push to the Branch (`git push origin feature/AmazingFeature`)
5. Open a Pull Request
Please ensure you read our [Contributing Guidelines](CONTRIBUTING.md) and [Code of Conduct](CODE_OF_CONDUCT.md) before making a contribution.
## 📜 License
This project is licensed under the Apache License 2.0 - see the [LICENSE](LICENSE) file for details.
## 👤 Author
**Dr. Saad Laouadi**
- 🌐 Website: [quantcodingversity.com](https://quantcodingversity.com)
- 💼 LinkedIn: [@saadlaouadi](https://linkedin.com/in/saadlaouadi)
- 🐦 Twitter: [@saadlaouadi](https://twitter.com/saadlaouadi)
## ⭐ Show your support
If you find this project helpful, please consider giving it a star! ⭐️
## 📝 Citation
If you use this work in your research, please cite:
```bibtex
@misc{laouadi2024mlmetrics,
author = {Laouadi, Saad},
title = {Machine Learning Evaluation Metrics},
year = {2025},
publisher = {GitHub},
journal = {GitHub repository},
howpublished = {\url{https://github.com/dr-saad-la/ml-evaluation-metrics}}
}
```
## 🙏 Acknowledgments
TO-DO
Made with ❤️ by Dr. Saad Laouadi