# Evaluate

> Evaluate - The Evaluate tabs provide key plots and statistics needed to judge a model's
> effectiveness, including ROC Curve, Lift Chart, and Forecasting Accuracy.

This Markdown file sits beside the HTML page at the same path (with a `.md` suffix). It summarizes the topic and lists links for tools and LLM context.

Companion generated at `2026-04-24T16:03:56.584297+00:00` (UTC).

## Primary page

- [Evaluate](https://docs.datarobot.com/en/docs/classic-ui/modeling/analyze-models/evaluate/index.html): Full documentation for this topic (HTML).

## Related documentation

- [Classic UI documentation](https://docs.datarobot.com/en/docs/classic-ui/index.html): Linked from this page.
- [Modeling](https://docs.datarobot.com/en/docs/classic-ui/modeling/index.html): Linked from this page.
- [Model insights](https://docs.datarobot.com/en/docs/classic-ui/modeling/analyze-models/index.html): Linked from this page.
- [Accuracy Over Space](https://docs.datarobot.com/en/docs/classic-ui/modeling/special-workflows/location-ai/lai-insights.html): Linked from this page.
- [Accuracy over Time](https://docs.datarobot.com/en/docs/classic-ui/modeling/analyze-models/evaluate/aot-classic.html): Linked from this page.
- [Advanced Tuning](https://docs.datarobot.com/en/docs/classic-ui/modeling/analyze-models/evaluate/adv-tuning.html): Linked from this page.
- [Anomaly Assessment](https://docs.datarobot.com/en/docs/classic-ui/modeling/analyze-models/evaluate/anom-viz.html): Linked from this page.
- [Confusion Matrix for multiclass projects](https://docs.datarobot.com/en/docs/classic-ui/modeling/analyze-models/evaluate/multiclass.html): Linked from this page.
- [confusion matrix](https://docs.datarobot.com/en/docs/classic-ui/modeling/analyze-models/evaluate/roc-curve-tab/confusion-matrix-classic.html): Linked from this page.
- [ROC Curve](https://docs.datarobot.com/en/docs/classic-ui/modeling/analyze-models/evaluate/roc-curve-tab/index.html): Linked from this page.
- [Feature Effects](https://docs.datarobot.com/en/docs/classic-ui/modeling/analyze-models/understand/feature-effects-classic.html): Linked from this page.
- [Forecasting Accuracy](https://docs.datarobot.com/en/docs/classic-ui/modeling/analyze-models/evaluate/forecast-acc.html): Linked from this page.
- [Forecast vs Actual](https://docs.datarobot.com/en/docs/classic-ui/modeling/analyze-models/evaluate/fore-act.html): Linked from this page.
- [Lift Chart](https://docs.datarobot.com/en/docs/classic-ui/modeling/analyze-models/evaluate/lift-chart-classic.html): Linked from this page.
- [Period Accuracy](https://docs.datarobot.com/en/docs/classic-ui/modeling/analyze-models/evaluate/period-acc-classic.html): Linked from this page.
- [Residuals](https://docs.datarobot.com/en/docs/classic-ui/modeling/analyze-models/evaluate/residuals-classic.html): Linked from this page.
- [Series Insights (clustering)](https://docs.datarobot.com/en/docs/classic-ui/modeling/analyze-models/evaluate/series-insights-classic.html): Linked from this page.
- [Series Insights (multiseries)](https://docs.datarobot.com/en/docs/classic-ui/modeling/analyze-models/evaluate/series-insights-multi.html): Linked from this page.
- [Stability](https://docs.datarobot.com/en/docs/classic-ui/modeling/analyze-models/evaluate/stability-classic.html): Linked from this page.
- [Training Dashboard](https://docs.datarobot.com/en/docs/classic-ui/modeling/analyze-models/evaluate/training-dash.html): Linked from this page.

## Documentation content

# Evaluate

The Evaluate tabs provide key plots and statistics needed to judge and interpret a model’s effectiveness:

| Leaderboard tab | Description | Source |
| --- | --- | --- |
| Accuracy Over Space | Provides a spatial residual mapping within an individual model. | Validation, Cross-Validation, Holdout (selectable) |
| Accuracy over Time | Visualizes how predictions change over time. | Computed separately for each backtest and the Holdout fold and can be viewed in the UI. Plots can be computed on both Validation and Training data. |
| Advanced Tuning | Visualizes how predictions change over time. | Internal grid search set |
| Anomaly Assessment | Plots data for the selected backtest and provides SHAP explanations for up to 500 anomalous points. | Computed separately for each backtest and the Holdout fold and can be viewed in the UI. Plots can be computed on both Validation and Training data. |
| Anomaly over Time | Plots how anomalies occur across the timeline of your data. | Computed separately for each backtest and the Holdout fold and can be viewed in the UI. Plots can be computed on both Validation and Training data. |
| Confusion Matrix for multiclass projects | Compares actual data values with predicted data values in multiclass projects. | Validation, Cross-Validation, or Holdout (selectable). For binary classification projects, use the confusion matrix on the ROC Curve tab. |
| Feature Fit | Removed. See Feature Effects. |  |
| Forecasting Accuracy | Provides a visual indicator of how well a model predicts at each forecast distance in the project’s forecast window. | Computed separately for each backtest and the Holdout fold; only the validation subset of each fold is scored. Validation predictions are filtered by the forecast distance and the metrics are computed on the filtered predictions. UI/API does not provide access to individual backtests but rather to validation (backtest 0=most recent backtest), backtesting (averaged across all backtests), and Holdout. |
| Forecast vs Actual | Compares how different predictions behave at different forecast points to different times in the future. | Computed separately for each backtest and the Holdout fold and can be viewed in the UI. Plots can be computed on both Validation and training data. |
| Lift Chart | Depicts how well a model segments the target population and how capable it is of predicting the target. | Validation, Cross-Validation, Holdout (selectable) |
| Period Accuracy | View model performance over periods within the training dataset. | Validation, Holdout (selectable). Computed separately for each backtest and Holdout. |
| Residuals | Clearly visualizes the predictive performance and validity of a regression model. | Validation, Cross-Validation, Holdout (selectable) |
| ROC Curve | Explores classification, performance, and statistics related to a selected model at any point on the probability scale. | Validation data |
| Series Insights (clustering) | Provides information on the cluster to which each series belongs, along with series information, including rows and dates. Histograms for each cluster show the number of series, the number of total rows, and the percentage of the dataset that belongs to that cluster. | Computed for each series in the clustering backtest. |
| Series Insights (multiseries) | Provides series-specific information. | Computed separately for each backtest and the Holdout fold; only the validation subset of each fold is scored. Validation predictions are filtered by the forecast distance and the metrics are computed on the filtered predictions. UI/API does not provide access to individual backtests but rather to validation (backtest 0=most recent backtest), backtesting (averaged across all backtests), and Holdout. |
| Stability | Provides an at-a-glance summary of how well a model performs on different backtests. | Computed separately for each backtest and the Holdout fold; only the validation subset of each fold is scored. |
| Training Dashboard | Provides an understanding about training activity, per iteration, for Keras-based models. | Training, but validated on an internal holdout of the training data. |