📊 Evaluation Metrics in Machine Learning 🤖

This collection includes various metrics for evaluating machine learning tasks like regression, classification, and clustering. These metrics are designed to help you assess your models' performance effectively.

📝 Table of Contents

• Introduction
• Implemented Metrics
• Usage
• Data
• Contributing
• License

🎉 Introduction

Evaluating how well machine learning models perform is vital. This collection provides a diverse set of metrics to analyze your models' effectiveness. By using these metrics, you can understand what your models do well and where they need improvement.

📈 Implemented Metrics

This collection currently covers three main types of tasks:

Regression Metrics

• Mean Squared Error (MSE)
• Root Mean Squared Error (RMSE)
• Mean Absolute Error (MAE)
• R-squared (R2) Score
• Adjusted R-squared (R2) Score
• Pearson Correlation
• Spearman Correlation

Classification Metrics

• Confusion Matrix
• Accuracy Score
• Precision Score
• F-1 Score
• Recall Score
• Log Loss/Binary Cross Entropy Loss
• Area Under the ROC Curve (ROC AUC)
• Classification Report
• Average Precision
• Precision-Recall Curve

Clustering Metrics

• Silhouette Coefficient

Sequence Prediction Metrics

• Word Error Rate
• BLEU Score
• Perplexity

These metrics cater to various evaluation needs across different machine learning domains. Each metric is transparent and can be customized if necessary.

🚀 Usage

To utilize these metrics:

1. Clone the repository:

   git clone https://github.com/ajitsingh98/All-About-Performance-Metrics.git

2. Navigate to the directory:

   cd Evaluation-Metrics-In-Machine-Learning-Problems-Python

3. Analyze the results to gain insights into your model's performance.

📊 Data

Sample data files in the data/ directory (e.g., Churn_Modelling.csv, HousingData.csv, Mall_Customers.csv) are provided. You can use these to test the performance metrics or substitute them with your own data.

🤝 Contributing

Contributions are welcome! If you have suggestions or additional metrics to include:

1. Fork the repository.
2. Create a new branch for your changes.
3. Make the necessary changes and commit them.
4. Push your changes to your forked repository.
5. Submit a pull request.

Your contributions will be reviewed and merged if approved.

📄 License

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

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I hope this collection of performance metrics proves valuable for evaluating your machine learning models. Feel free to explore, experiment, and contribute to enhancing the metrics further. If you have any questions or encounter issues, don't hesitate to reach out. Happy modeling and evaluating! 😊🌟