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NextGen experience > AI experimentation > Time-aware experiments > Evaluate with model insights

Evaluate with model insights

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, but may include the insights listed in the table below.

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 experiments.

Insight / tab Description Problem type Sliced insights?
Accuracy Over Time
Performance tab		 Visualizes how predictions change over time. Time-aware
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 Time
Performance tab		 Visualizes where anomalies occur across the timeline of your data. Time-aware
Blueprint
Details tab		 Provides a graphical representation of data preprocessing and parameter settings. All
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
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
Forecast vs Actual
Performance tab		 Predicts multiple values for each point in time (forecast distances). Time series
Forecasting Accuracy
Performance tab		 Provides a visual indicator of how well a model predicts at each forecast distance. Time-aware
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 All
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
Model Info
Details tab		 Provides general model and performance information. All
Period Accuracy
Performance tab		 Shows model performance over periods within the training dataset. Time-aware
Metric Scores Performance tab
Displays results for all supported metrics. All
Related Assets
Artifacts tab		 Lists all apps, deployments, and registered models associated with the model; launches no-code apps creation or model registration. All
ROC Curve
Performance tab		 Provides tools for exploring classification, performance, and statistics related to a model. Classification ✔
Series Insights
Performance tab		 Provides series-specific information for multiseries experiments. Time series
Stability
Performance tab		 Provides a summary of how well a model performs on different backtests. Time-aware
Insight Description Problem type Sliced insights?
Explanations
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
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 ✔
Performance
Accuracy Over Time Visualizes how predictions change over time. Time-aware
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 Time Visualizes where anomalies occur across the timeline of your data. 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
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
Period Accuracy Shows model performance over periods within the training dataset. Time-aware
ROC Curve Provides tools for exploring classification, performance, and statistics related to a model. 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
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
Artifacts
Compliance documentation Generates individualized documentation to provide comprehensive guidance on what constitutes effective model risk management.
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

What's next?

After selecting a model, you can, from within the experiment:

Updated January 8, 2025