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.
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 |
| Type |
|---|
| Input | Y Test (from Train-Test Split) + Predictions (from Inference) |
| Output | Metrics and charts (displayed in the Evaluation dashboard) |
