# Evaluate models
URL: https://docs.datarobot.com/en/docs/workbench/nxt-workbench/experiments/experiment-insights/index.html

> Describes how to use the visualization tools to evaluate predictive models in the DataRobot Workbench interface.

# Evaluate models

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

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