Configuration
| Option | Description | Default |
|---|---|---|
| Problem Type | classification or regression. Must match the model used. | classification |
| Metrics | Which metrics to compute. Leave empty to compute all defaults. | All |
Classification metrics
| Metric | Description |
|---|---|
| Accuracy | Fraction of predictions that are correct |
| Precision | Of all positive predictions, how many are actually positive |
| Recall | Of all actual positives, how many were correctly predicted |
| F1 | Harmonic mean of precision and recall |
| Confusion Matrix | Table showing true vs. predicted class counts |
Regression metrics
| Metric | Description |
|---|---|
| MAE | Mean Absolute Error — average absolute difference |
| MSE | Mean Squared Error — penalizes large errors more |
| RMSE | Root Mean Squared Error — in the same units as the target |
| R² | Proportion of variance explained by the model. 1.0 = perfect. |
Visualizations
| Problem Type | Charts |
|---|---|
| Classification | Class distribution, Confusion Matrix |
| Regression | Actual vs. Predicted scatter, Residuals plot |
Input / Output
| Type | |
|---|---|
| Input | Y Test (from Train-Test Split) + Predictions (from Inference) |
| Output | Metrics and charts (displayed in the Evaluation dashboard) |