Initial commit

skills/ml-model-trainer/assets/evaluation_report_template.md

@@ -0,0 +1,75 @@
+# 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.]