# Evaluate models > Evaluate models - Describes how to use the visualization tools to evaluate predictive models in the > DataRobot Workbench interface. 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 `2026-05-06T18:17:10.063276+00:00` (UTC). ## Primary page - [Evaluate models](https://docs.datarobot.com/en/docs/workbench/nxt-workbench/experiments/experiment-insights/index.html): Full documentation for this topic (HTML). ## Sections on this page - [Available insights](https://docs.datarobot.com/en/docs/workbench/nxt-workbench/experiments/experiment-insights/index.html#available-insights): In-page section heading. ## Related documentation - [NextGen UI documentation](https://docs.datarobot.com/en/docs/workbench/index.html): Linked from this page. - [Workbench](https://docs.datarobot.com/en/docs/workbench/nxt-workbench/index.html): Linked from this page. - [Predictive experiments](https://docs.datarobot.com/en/docs/workbench/nxt-workbench/experiments/index.html): Linked from this page. - [comparison](https://docs.datarobot.com/en/docs/workbench/nxt-workbench/experiments/manage-experiments/compare-models.html): Linked from this page. - [Model Leaderboard](https://docs.datarobot.com/en/docs/workbench/nxt-workbench/experiments/manage-experiments/leaderboard.html): Linked from this page. - [Sliced insights?](https://docs.datarobot.com/en/docs/reference/pred-ai-ref/sliced-insights.html): Linked from this page. - [Accuracy Over Space](https://docs.datarobot.com/en/docs/workbench/nxt-workbench/experiments/experiment-insights/acc-over-space.html): Linked from this page. - [Accuracy Over Time](https://docs.datarobot.com/en/docs/workbench/nxt-workbench/experiments/experiment-insights/aot.html): Linked from this page. - [Anomaly Assessment](https://docs.datarobot.com/en/docs/workbench/nxt-workbench/experiments/experiment-insights/anom-assessment.html): Linked from this page. - [Anomaly Over Space](https://docs.datarobot.com/en/docs/workbench/nxt-workbench/experiments/experiment-insights/anom-over-space.html): Linked from this page. - [Anomaly Over Time](https://docs.datarobot.com/en/docs/workbench/nxt-workbench/experiments/experiment-insights/anom-over-time.html): Linked from this page. - [Attention Maps](https://docs.datarobot.com/en/docs/workbench/nxt-workbench/experiments/experiment-insights/attention-map.html): Linked from this page. - [Blueprint](https://docs.datarobot.com/en/docs/workbench/nxt-workbench/experiments/experiment-insights/model-blueprint.html): Linked from this page. - [Cluster Insights](https://docs.datarobot.com/en/docs/workbench/nxt-workbench/experiments/experiment-insights/cluster-insights.html): Linked from this page. - [Coefficients](https://docs.datarobot.com/en/docs/workbench/nxt-workbench/experiments/experiment-insights/coefficients.html): Linked from this page. - [Compliance documentation](https://docs.datarobot.com/en/docs/workbench/nxt-workbench/experiments/experiment-insights/compliance-report.html): Linked from this page. - [Confusion matrix](https://docs.datarobot.com/en/docs/workbench/nxt-workbench/experiments/experiment-insights/confusion-matrix.html): Linked from this page. - [Downloads](https://docs.datarobot.com/en/docs/workbench/nxt-workbench/experiments/experiment-insights/downloads.html): Linked from this page. - [Eureqa Models](https://docs.datarobot.com/en/docs/workbench/nxt-workbench/experiments/experiment-insights/eureqa.html): Linked from this page. - [Feature Effects](https://docs.datarobot.com/en/docs/workbench/nxt-workbench/experiments/experiment-insights/feature-effects.html): Linked from this page. - [Feature Impact](https://docs.datarobot.com/en/docs/workbench/nxt-workbench/experiments/experiment-insights/feature-impact.html): Linked from this page. - [Forecasting Accuracy](https://docs.datarobot.com/en/docs/workbench/nxt-workbench/experiments/experiment-insights/fcast-accuracy.html): Linked from this page. - [Forecast vs Actual](https://docs.datarobot.com/en/docs/workbench/nxt-workbench/experiments/experiment-insights/fcast-v-actual.html): Linked from this page. - [Image Embeddings](https://docs.datarobot.com/en/docs/workbench/nxt-workbench/experiments/experiment-insights/image-embeddings.html): Linked from this page. - [Individual Prediction Explanations](https://docs.datarobot.com/en/docs/workbench/nxt-workbench/experiments/experiment-insights/shap-predex.html): Linked from this page. - [Individual Prediction Explanations (XEMP)](https://docs.datarobot.com/en/docs/workbench/nxt-workbench/experiments/experiment-insights/xemp-predex.html): Linked from this page. - [Lift Chart](https://docs.datarobot.com/en/docs/workbench/nxt-workbench/experiments/experiment-insights/lift-chart.html): Linked from this page. - [Log](https://docs.datarobot.com/en/docs/workbench/nxt-workbench/experiments/experiment-insights/log.html): Linked from this page. - [Metric Scores](https://docs.datarobot.com/en/docs/workbench/nxt-workbench/experiments/experiment-insights/metric-scores.html): Linked from this page. - [Model Info](https://docs.datarobot.com/en/docs/workbench/nxt-workbench/experiments/experiment-insights/model-info.html): Linked from this page. - [Model Iterations](https://docs.datarobot.com/en/docs/workbench/nxt-workbench/experiments/experiment-insights/incremental.html): Linked from this page. - [Multilabel: Per-Label Metrics](https://docs.datarobot.com/en/docs/workbench/nxt-workbench/experiments/experiment-insights/multilabel.html): Linked from this page. - [Neural Network Visualizer](https://docs.datarobot.com/en/docs/workbench/nxt-workbench/experiments/experiment-insights/neural-net.html): Linked from this page. - [Period Accuracy](https://docs.datarobot.com/en/docs/workbench/nxt-workbench/experiments/experiment-insights/period-accuracy.html): Linked from this page. - [Rating Tables](https://docs.datarobot.com/en/docs/workbench/nxt-workbench/experiments/experiment-insights/rating-tables.html): Linked from this page. - [Related Assets](https://docs.datarobot.com/en/docs/workbench/nxt-workbench/experiments/experiment-insights/rel-assets.html): Linked from this page. - [no-code apps creation](https://docs.datarobot.com/en/docs/workbench/nxt-workbench/experiments/no-code-apps/index.html): Linked from this page. - [model registration](https://docs.datarobot.com/en/docs/workbench/nxt-registry/index.html): Linked from this page. - [Residuals](https://docs.datarobot.com/en/docs/workbench/nxt-workbench/experiments/experiment-insights/residuals.html): Linked from this page. - [ROC Curve](https://docs.datarobot.com/en/docs/workbench/nxt-workbench/experiments/experiment-insights/roc-curve.html): Linked from this page. - [Series Insights](https://docs.datarobot.com/en/docs/workbench/nxt-workbench/experiments/experiment-insights/series-insights.html): Linked from this page. - [SHAP Distributions: Per Feature](https://docs.datarobot.com/en/docs/workbench/nxt-workbench/experiments/experiment-insights/shap-distribution.html): Linked from this page. - [Stability](https://docs.datarobot.com/en/docs/workbench/nxt-workbench/experiments/experiment-insights/stability.html): Linked from this page. - [Word Cloud](https://docs.datarobot.com/en/docs/workbench/nxt-workbench/experiments/experiment-insights/word-cloud.html): Linked from this page. - [Blueprint](https://docs.datarobot.com/en/docs/api/dev-learning/python/modeling/blueprint.html): Linked from this page. ## Documentation content Model insights help to interpret, explain, and validate what drives a model’s predictions. Using these tools can help to assess what to do in your next experiment. Available insights are dependent on experiment type as well as the experiment view (single versus [comparison](https://docs.datarobot.com/en/docs/workbench/nxt-workbench/experiments/manage-experiments/compare-models.html)). Click on a model from the [Model Leaderboard](https://docs.datarobot.com/en/docs/workbench/nxt-workbench/experiments/manage-experiments/leaderboard.html) to access insights. ## Available insights To see a model's insights, click on the model in the left-pane Leaderboard—the Model Overview opens. From here, all available experiment insights are available, grouped by purpose and answering: - Explanations : What did the model learn? - Performance : How good is the model? - Details : How was the model built? - Artifacts : What are the assets from the model? Use search to filter insights by name and/or description. The results also mark which group the insight belongs to. Note that different insights are available for predictive and time-aware experiments, as noted in the table. **Insights: alphabetical:** Insight / tab Description Problem type Sliced insights? Compare available? Accuracy Over Space Performance tab Reveals spatial patterns in prediction errors and visualizes prediction errors across data partitions on a map visualization. Geospatial Accuracy Over Time Performance tab Visualizes how predictions change over time. Time-aware predictive Anomaly Assessment Performance tab Plots data for the selected backtest and provides, below the visualization, SHAP explanations for up to 500 anomalous points. Time series Anomaly Over Space Performance tab Maps anomaly scores based on a dataset's location features. Geospatial Anomaly Over Time Performance tab Visualizes where anomalies occur across the timeline of your data. Time-aware predictive Attention Maps Explanations tab Highlights regions of an image according to its importance to a model's prediction. Visual AI, time-aware predictive Blueprint Details tab Provides a graphical representation of data preprocessing and parameter settings. All Cluster Insights Explanations tab Visualizes the groupings of data that result from modeling with learning type set to clustering. Predictive clustering Coefficients Explanations tab Provides a visual indicator of the relative effects of the 30 most important variables. All; linear models only Compliance documentation Generates individualized documentation to provide comprehensive guidance on what constitutes effective model risk management. All Confusion matrix Performance tab Compares actual with predicted values in multiclass classification problems to identify class mislabeling. Classification, time-aware Downloads Artifacts tab Download model artifacts in a single ZIP file. All Eureqa Models Details tab Uses a proprietary Eureqa machine learning algorithm to construct models that balance predictive accuracy against complexity. All, no multiclass Feature Effects Explanations tab Conveys how changes to the value of each feature change model predictions All ✔ Feature Impact Explanations tab Shows which features are driving model decisions. All ✔ ✔ Forecasting Accuracy Performance tab Depicts how well a model predicts at each forecast distance in the experiment's forecast window. Time series, Time-aware predictive Forecast vs Actual Performance tab Predicts multiple values for each point in time (forecast distances). Time series Image Embeddings Explanations tab Shows projections of images in two dimensions to see visual similarity between a subset of images and help identify outliers. Visual AI, time-aware predictive Individual Prediction Explanations Explanations tab Estimates how much each feature contributes to a given prediction, with values based on difference from the average. Binary classification, regression ✔ Individual Prediction Explanations (XEMP) Estimates how much each feature contributes to a given prediction, with values based on difference from the average. Binary classification, regression ✔ Lift Chart Performance tab Depicts how well a model segments the target population and how capable it is of predicting the target. All ✔ ✔ Log ) Details tab Lists operational status results for modeling tasks. All Metric Scores Performance tab Displays results for all supported metrics. All Model Info Details tab Provides general model and performance information. All Model Iterations Details tab Compares trained iterations in incremental learning experiments. Binary classification, regression Multilabel: Per-Label Metrics Performance tab Summarizes performance across different label values of the prediction threshold. Multilabel classification Neural Network Visualizer Details tab Provides a visual breakdown of each layer in the model's neural network. Visual AI, time-aware predictive Period Accuracy Performance tab Shows model performance over periods within the training dataset. Time-aware predictive Rating Tables Details tab Trains child models from downloaded, validated parameters with modified coefficients. No time series; GAM, GA2M, or Frequency/Severity models only Related Assets Artifacts tab Lists all apps, deployments, and registered models associated with the model; launches no-code apps creation or model registration . All Residuals Performance tab Provides scatter plots and a histogram for understanding model predictive performance and validity. Regression ✔ ROC Curve Performance tab Provides tools for exploring classification, performance, and statistics related to a model. Binary classification ✔ ✔ Series Insights Performance tab Provides series-specific information for multiseries experiments. Time series SHAP Distributions: Per Feature Explanations tab Displays, via a violin plot, the distribution of SHAP values and feature values to aid in the analysis of how feature values influence predictions. Binary classification, regression ✔ Stability Performance tab Provides a summary of how well a model performs on different backtests. Time-aware predictive Word Cloud Explanations tab Visualize how text features influence model predictions. Binary classification, regression **Insights: by tab:** Insight Description Problem type Sliced insights? Compare available? Explanations Attention Maps Highlights regions of an image according to its importance to a model's prediction. Visual AI, time-aware predictive Cluster Insights Visualizes the groupings of data that result from modeling with learning type set to clustering. Predictive clustering Coefficients Provides a visual indicator of the relative effects of the 30 most important variables. All; linear models only Feature Effects Conveys how changes to the value of each feature change model predictions All ✔ Feature Impact Shows which features are driving model decisions. All ✔ ✔ Forecasting Accuracy Depicts how well a model predicts at each forecast distance in the experiment's forecast window. Time series Image Embeddings Shows projections of images in two dimensions to see visual similarity between a subset of images and help identify outliers. Visual AI, time-aware predictive Individual Prediction Explanations Estimates how much each feature contributes to a given prediction, with values based on difference from the average. Binary classification, regression ✔ Individual Prediction Explanations (XEMP) Estimates how much each feature contributes to a given prediction, with values based on difference from the average. Binary classification, regression ✔ SHAP Distributions: Per Feature Displays, via a violin plot, the distribution of SHAP values and feature values to aid in the analysis of how feature values influence predictions. Binary classification, regression ✔ Word Cloud Visualize how text features influence model predictions. Binary classification, regression Performance Accuracy Over Space Reveals spatial patterns in prediction errors and visualizes prediction errors across data partitions on a map visualization. Geospatial Accuracy Over Time Visualizes how predictions change over time. Time-aware predictive Anomaly Assessment Plots data for the selected backtest and provides, below the visualization, SHAP explanations for up to 500 anomalous points. Time series Anomaly Over Space Maps anomaly scores based on a dataset's location features. Geospatial Anomaly Over Time Visualizes where anomalies occur across the timeline of your data. Time-aware predictive Confusion matrix Compares actual with predicted values in multiclass classification problems to identify class mislabeling. Classification, time-aware Forecast vs Actual Predicts multiple values for each point in time (forecast distances). Time series Forecasting Accuracy Provides a visual indicator of how well a model predicts at each forecast distance. Time-aware predictive Lift Chart Depicts how well a model segments the target population and how capable it is of predicting the target. All ✔ ✔ Metric Scores Displays results for all supported metrics. All Multilabel: Per-Label Metrics Summarizes performance across different label values of the prediction threshold. Multilabel classification Period Accuracy Shows model performance over periods within the training dataset. Time-aware predictive Residuals Provides scatter plots and a histogram for understanding model predictive performance and validity. Regression ✔ ROC Curve Provides tools for exploring classification, performance, and statistics related to a model. Binary classification ✔ ✔ Series Insights Provides series-specific information for multiseries experiments. Time series Stability Provides a summary of how well a model performs on different backtests. Time-aware predictive Details Blueprint Provides a graphical representation of data preprocessing and parameter settings. All Eureqa Models Uses a proprietary Eureqa machine learning algorithm to construct models that balance predictive accuracy against complexity. All, no multiclass Log Lists operational status results for modeling tasks. All Model Info Provides general model and performance information. All Model Iterations Compares trained iterations in incremental learning experiments. Binary classification, regression Neural Network Visualizer Provides a visual breakdown of each layer in the model's neural network. Visual AI, time-aware predictive Rating Tables Trains child models from downloaded, validated parameters with modified coefficients. No time series; GAM, GA2M, or Frequency/Severity models only Artifacts Compliance documentation Generates individualized documentation to provide comprehensive guidance on what constitutes effective model risk management. All Downloads Download model artifacts in a single ZIP file. All Related Assets Lists all apps, deployments, and registered models associated with the model; launches no-code apps creation or model registration . All