https://github.com/sayande01/model_evaluation

This notebook explores key metrics for evaluating machine learning models: Accuracy, Precision, Recall, True Positive Ratio (TPR), False Positive Ratio (FPR), and the ROC Curve. It offers detailed explanations, calculations, and visualizations, demonstrating their roles in assessing classification model performance.
https://github.com/sayande01/model_evaluation

Last synced: 2 months ago
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This notebook explores key metrics for evaluating machine learning models: Accuracy, Precision, Recall, True Positive Ratio (TPR), False Positive Ratio (FPR), and the ROC Curve. It offers detailed explanations, calculations, and visualizations, demonstrating their roles in assessing classification model performance.

• Host: GitHub
• URL: https://github.com/sayande01/model_evaluation
• Owner: sayande01
• Created: 2024-07-21T13:22:02.000Z (11 months ago)
• Default Branch: main
• Last Pushed: 2024-07-21T13:36:21.000Z (11 months ago)
• Last Synced: 2025-02-13T02:38:56.137Z (4 months ago)
• Language: Jupyter Notebook
• Size: 290 KB
• Stars: 0
• Watchers: 1
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

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README

        
### Title

Understanding Key Metrics in Machine Learning: Accuracy, Precision, Recall, TPR, FPR, and ROC Curve

### Description

This notebook delves into fundamental metrics used in evaluating machine learning models: Accuracy, Precision, Recall, True Positive Ratio (TPR), False Positive Ratio (FPR), and the ROC Curve. Each metric plays a crucial role in understanding the performance of classification models. The notebook provides detailed explanations, calculations, and visualizations to illustrate these concepts effectively.

### Objective

The objective of this notebook is to:

1. Explain the concepts of Accuracy, Precision, Recall, True Positive Ratio (TPR), False Positive Ratio (FPR), and the ROC Curve.

2. Demonstrate the importance and use-cases of each metric in evaluating classification models.

3. Provide practical examples and visualizations to enhance understanding.

4. Equip readers with the knowledge to interpret and apply these metrics in their own machine learning projects.