The AI Model Evaluation Template typically includes the following components:
* Description of the problem being addressed
* Type of machine learning algorithm used (e.g., supervised, unsupervised, reinforcement) * Data sources and preprocessing steps - **Evaluation Metrics**: * **Accuracy**: measures the proportion of correct predictions * **Precision**: measures the proportion of true positives among all positive predictions * **Recall**: measures the proportion of true positives among all actual positives * **F1-score**: harmonic mean of precision and recall * **Mean Absolute Error (MAE)**: average absolute difference between predicted and actual values * **Root Mean Squared Error (RMSE)**: square root of the average squared difference between predicted and actual values * **Classification Report**: provides a detailed breakdown of true positives, false positives, true negatives, and false negatives for each class - **Model Performance**: * **Confusion Matrix**: table showing the number of true positives, false positives, true negatives, and false negatives * **ROC Curve**: plot of the true positive rate against the false positive rate at different thresholds * **PR Curve**: plot of precision against recall at different thresholds - **Model Evaluation**: * **Cross-validation**: measures model performance on unseen data using techniques like k-fold cross-validation or stratified k-fold cross-validation * **Hyperparameter Tuning**: explores the impact of hyperparameters on model performance - **Comparison to Baseline Models**: * Compare the proposed AI model's performance against a baseline model (e.g., naive Bayes, decision tree) - **Sensitivity Analysis**: * Evaluate the model's robustness to changes in input data or hyperparameters
Example of an AI Model Evaluation Template:
Description | |
— | — |
Model Overview | Predicting customer churn using a Random Forest classifier |
Evaluation Metrics | Accuracy, Precision, Recall, F1-score, MAE, RMSE |
Model Performance | Confusion Matrix, ROC Curve, PR Curve |
Model Evaluation | k-fold Cross-validation (k=5), Hyperparameter Tuning (grid search) |
Comparison to Baseline Models | Naive Bayes classifier as baseline model |
Sensitivity Analysis | Evaluating the impact of input feature selection on model performance |
This template provides a structured approach to evaluating and assessing the performance of an AI or ML model, enabling data scientists and developers to make informed decisions about its deployment.