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NextGen experience > AI Experimentation > Predictive 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 as well as experiment view:

To see a model's insights, click on the model in the left-pane Leaderboard. Note that different insights are available for time-aware experiments.

Available insights

Insight Description Problem type Sliced insights? Compare available?
Accuracy Over Space Reveals spatial patterns in prediction errors and visualizes prediction errors across data partitions on a map visualization.
Anomaly Over Space Maps anomaly scores based on a dataset's location features.
Blueprint Provides a graphical representation of data preprocessing and parameter settings. All
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
Compliance documentation Generates individualized documentation to provide comprehensive guidance on what constitutes effective model risk management. All
Confusion matrix Compares actual with predicted values in multiclass classification problems to identify class mislabeling. Classification, time-aware
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 ✔ ✔
Individual Prediction Explanations Estimates how much each feature contributes to a given prediction, with values based on difference from the average. Binary classification, regression ✔
Lift Chart Depicts how well a model segments the target population and how capable it is of predicting the target. All ✔ ✔
Model Iterations Compares trained iterations in incremental learning experiments. Binary classification, regression
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 ✔ ✔
SHAP Distributions: Per Feature Displays, via a 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

What's next?

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

Updated November 3, 2024