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‎README.md‎

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# Evaluation Metrics in Machine Learning
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This repository contains a comprehensive collection of performance metrics for various machine learning tasks, including regression, classification, and clustering. These metrics have been implemented from scratch to provide a reliable and customizable way of evaluating the performance of your machine learning models.
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# 📊 Evaluation Metrics in Machine Learning 🤖
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## Table of Contents
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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.
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## 📝 Table of Contents
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- [Introduction](#introduction)
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- [Implemented Metrics](#implemented-metrics)
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- [Contributing](#contributing)
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- [License](#license)
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## Introduction
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## 🎉 Introduction
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Evaluating the performance of machine learning models is a crucial step in the development and assessment of their effectiveness. This repository aims to provide a wide range of performance metrics that can be applied to various machine learning tasks. By using these metrics, you can measure and analyze the performance of your models, gain insights into their strengths and weaknesses, and make informed decisions about improving them.
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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.
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## Implemented Metrics
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## 📈 Implemented Metrics
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The repository currently includes the following performance metrics:
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This collection currently covers three main types of tasks:
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### [Regression Metrics](regression_metrics.ipynb)
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- Mean Absolute Error (MAE)
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- R-squared (R2) Score
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- Adjusted R-squared (R2) Score
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- Pearson correlation
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- spearman correlation
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- Pearson Correlation
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- Spearman Correlation
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### [Classification Metrics](classification_metrics.ipynb)
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- Confusion Matrix
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- Confusion Matrix
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- Accuracy Score
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- Precision Score
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- F-1 Score
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- Recall Score
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- Log Loss/Binary Cross Entropy Loss
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- Area Under the Receiver Operating Characteristic Curve (ROC AUC)
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- Classification report
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- Average precision
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- precision-recall curve
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- Log Loss/Binary Cross Entropy Loss
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- Area Under the ROC Curve (ROC AUC)
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- Classification Report
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- Average Precision
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- Precision-Recall Curve
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### [Clustering Metrics](clustering_metrics.ipynb)
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- Silhouette Coefficient
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### [Sequence Prediction](sequence_model_evaluation_metrics_nlp.ipynb)
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### [Sequence Prediction Metrics](sequence_model_evaluation_metrics_nlp.ipynb)
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- Word Error Rate
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- BLEU Score
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- Perplexity
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These metrics cover a wide range of evaluation needs and can be utilized across different machine learning domains. Each metric has been implemented from scratch, ensuring transparency and allowing for customization if needed.
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These metrics cater to various evaluation needs across different machine learning domains. Each metric is transparent and can be customized if necessary.
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## Usage
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## 🚀 Usage
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To use the performance metrics in this repository, follow these steps:
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To utilize these metrics:
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1. Clone the repository to your local machine:
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1. Clone the repository:
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```bash
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git clone https://github.com/ajitsingh98/All-About-Performance-Metrics.git
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```
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2. Navigate to the repository directory:
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2. Navigate to the directory:
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```bash
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cd All-About-Performance-Metrics
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cd Evaluation-Metrics-In-Machine-Learning-Problems-Python
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```
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3. Analyze the results and utilize the metrics to gain insights into your model's performance.
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## Data
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3. Analyze the results to gain insights into your model's performance.
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This repository also includes sample data files that can be used to test the performance metrics. The data files are stored in the `data/` directory and are labeled according to the task they correspond to (e.g., `Churn_Modelling.csv`, `HousingData.csv`, `Mall_Customers.csv`).
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## 📊 Data
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Feel free to use these sample datasets to assess the performance metrics or substitute them with your own data to evaluate your models effectively.
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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.
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## Contributing
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## 🤝 Contributing
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Contributions to this repository are welcome! If you have any suggestions, improvements, or additional performance metrics that you would like to include, please follow these steps:
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Contributions are welcome! If you have suggestionsor additional metrics to include:
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1. Fork the repository.
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2. Create a new branch for your feature or enhancement.
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2. Create a new branch for your changes.
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3. Make the necessary changes and commit them.
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4. Push your changes to your forked repository.
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5. Submit a pull request, explaining the purpose and benefits of your changes.
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5. Submit a pull request.
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Your contributions will be reviewed, and upon approval, they will be merged into the main repository.
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Your contributions will be reviewed and merged if approved.
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## License
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## 📄 License
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This repository is licensed under the MIT License. See the [LICENSE](LICENSE) file for more details.
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This repository is licensed under the MIT License. See the [LICENSE](LICENSE) file for details.
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I hope that this repository and the included performance metrics will be valuable tools in evaluating the effectiveness of your machine learning models. Feel free to explore, experiment, and contribute to further improve the available metrics. If you have any questions or encounter any issues, please don't hesitate to reach out to me. Happy modeling and evaluating!
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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! 😊🌟

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