6.5 KiB
6.5 KiB
Model Evaluation Report
This report summarizes the evaluation of a machine learning model trained using the ML Model Trainer Plugin. It provides key metrics and insights into the model's performance.
1. Model Information
- Model Name: [Insert Model Name Here, e.g., "Customer Churn Prediction v1"]
- Model Type: [Insert Model Type Here, e.g., "Logistic Regression", "Random Forest"]
- Training Date: [Insert Date of Training Here, e.g., "2023-10-27"]
- Plugin Version: [Insert Plugin Version Here, find in plugin details]
- Dataset Used for Training: [Insert Dataset Name/Description Here, e.g., "Customer Transaction Data"]
2. Dataset Details
- Training Set Size: [Insert Number of Training Samples Here, e.g., "10,000"]
- Validation Set Size: [Insert Number of Validation Samples Here, e.g., "2,000"]
- Testing Set Size: [Insert Number of Testing Samples Here, e.g., "3,000"]
- Features Used: [List the features used for training. E.g., Age, Income, Location, etc.]
- Target Variable: [Specify the target variable. E.g., Customer Churn (Yes/No)]
3. Training Parameters
- Parameters: [List of the hyper parameters used for the model. E.g., learning rate, number of estimators, etc.]
- Cross-Validation Strategy: [Describe the cross-validation strategy used (e.g., k-fold cross-validation with k=5)]
- Optimization Metric: [Specify the metric used for optimization during training (e.g., Accuracy, F1-score)]
4. Performance Metrics
4.1. Overall Performance
| Metric | Value | Description |
|---|---|---|
| Accuracy | [Insert Accuracy Here] | Percentage of correctly classified instances. Example: 0.85 means 85% of predictions were correct. |
| Precision | [Insert Precision Here] | Of all instances predicted as positive, what percentage were actually positive? Example: 0.78 means 78% of instances predicted as positive were actually positive. |
| Recall | [Insert Recall Here] | Of all actual positive instances, what percentage were correctly predicted? Example: 0.92 means 92% of all actual positive instances were correctly predicted. |
| F1-Score | [Insert F1-Score Here] | Harmonic mean of precision and recall. Provides a balanced measure of the model's performance. Example: 0.84 represents the harmonic mean of precision and recall. |
| AUC | [Insert AUC Here] | Area Under the Receiver Operating Characteristic (ROC) curve. Measures the model's ability to distinguish between positive and negative classes. Example: 0.95 indicates excellent discrimination between classes. |
4.2. Detailed Performance (Per Class)
[If applicable, include a table showing performance metrics for each class. For example, in a binary classification problem (Churn/No Churn), show precision, recall, and F1-score for each class.]
| Class | Precision | Recall | F1-Score |
|---|---|---|---|
| [Class 1 Name] | [Value] | [Value] | [Value] |
| [Class 2 Name] | [Value] | [Value] | [Value] |
| ... | ... | ... | ... |
4.3. Confusion Matrix
[Include a confusion matrix showing the counts of true positives, true negatives, false positives, and false negatives. This can be represented as a table or an image.]
| Predicted Positive | Predicted Negative | |
|---|---|---|
| Actual Positive | [True Positives] | [False Negatives] |
| Actual Negative | [False Positives] | [True Negatives] |
5. Model Interpretation
- Feature Importance: [Discuss the most important features influencing the model's predictions. You can provide a ranked list of features and their importance scores.]
- Insights: [Describe any interesting insights gained from the model. For example, "Customers with high income and low usage are more likely to churn."]
6. Recommendations
- Model Improvements: [Suggest potential improvements to the model. For example, "Try using a different algorithm", "Add more features", "Tune hyperparameters."]
- Further Analysis: [Suggest further analysis that could be performed. For example, "Investigate the reasons for high false positive rates."]
- Deployment Considerations: [Discuss any considerations for deploying the model to production. For example, "Monitor the model's performance over time", "Retrain the model periodically with new data."]
7. Conclusion
[Summarize the overall performance of the model and its suitability for the intended purpose. State whether the model is ready for deployment or if further improvements are needed.]
8. Appendix (Optional)
- [Include any additional information, such as detailed code snippets, visualizations, or links to external resources.]