Skip to content

On-premise users: click in-app to access the full platform documentation for your version of DataRobot.

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, but may include the insights listed in the predictive modeling insights table below. 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?
Blueprint Provides a graphical representation of data preprocessing and parameter settings. All
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.
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.
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 ✔
Word Cloud Visualize how text features influence model predictions. Binary classification, regression

Leaderboard views

There are two "flavors" of Leaderboard available.

  • This page describes the insights available from the Experiment tab, which helps to understand and evaluate models from a single experiment.
  • See also the Comparison tab page, which allows you to compare up to three models of the same type (for example, binary or regression) from any number of experiments within a single Use Case. Access the comparison tool from the tab or the dropdown on the experiment name in the breadcrumbs.

What's next?

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

Updated April 23, 2024