> ## Documentation Index
> Fetch the complete documentation index at: https://docs.clickml.app/llms.txt
> Use this file to discover all available pages before exploring further.

# Model evaluation component

> Measure your trained model's performance with classification and regression metrics, confusion matrices, ROC curves, and residual plots.

The **Evaluation** component takes the true target values and the model's predictions and computes performance metrics and visualizations. It is always the last component in a pipeline.

## 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)      |