Model Evaluation та оцінка моделей

🎓 Classification Metrics

**Accuracy:** Overall correctness of the model (true positives + true negatives) divided by total predictions (true positives + true negatives + false positives + false negatives).

**Precision:** True Positives / (True Positives + False Positives) - measures the proportion of correct positive predictions out of all positive predictions.

**Recall:** True Positives / (True Positives + False Negatives) - measures the proportion of actual positive cases that were correctly identified.

**F1-Score:** Harmonious average of Precision and Recall, providing a balanced measure of model performance.

**ROC-AUC:** Area under the Receiver Operating Characteristic curve, used for ranking models based on their ability to distinguish between classes.

**Usage:** F1 is useful when you need to balance precision and recall. ROC-AUC is most appropriate when ranking models.

🔧 Regression Metrics

**MAE: Mean Absolute Error**, the average absolute difference between predicted and actual values.

**RMSE: Root Mean Squared Error**, the square root of the average squared difference between predicted and actual values – sensitive to outliers.

**Choice:** MAE is preferred when errors are consistently distributed, RMSE is better for cases where large errors have a disproportionate impact.

**R²: Coefficient of Determination**, represents the proportion of variance in the dependent variable that is explained by the model (ranging from 0 to 1).

**MAPE: Mean Absolute Percentage Error**, calculates the average percentage difference between predicted and actual values – useful for comparing models across different scales.

**Usage:** R² provides an overall measure of fit, MAPE offers a percentage-based comparison.

📚 Practical Examples

**Example 1: Classification Evaluation** – To evaluate a classification model, you first divide your dataset into training and test sets.

**Training:** Train the model on the training data.

**Evaluation:** Calculate Precision, Recall, F1-score, and generate a Confusion Matrix using the test data.

**Confusion Matrix:** A visualization of errors, showing true positives, false positives, true negatives, and false negatives.

**Example 2: Cross-Validation for Evaluation** – To assess model robustness, use cross-validation techniques.

**K-fold:** Divide the dataset into K folds (e.g., 5 or 10). Train on K-1 folds and test on the remaining fold.

**CV:** Perform this K-fold process multiple times, rotating which fold is used for testing each time.

**Evaluation:** Calculate the average metrics across all K iterations to obtain a more reliable estimate of model performance.

Try it live

Everything above runs in your browser — open Model Evaluation ROC Space and change the parameters while it is running. Nothing is installed, nothing is uploaded, the whole model lives in one tab.