When you select a model, DataRobot makes available a large selection of insights, grouped by purpose, appropriate for that model.
The model Leaderboard is a list of models ranked by the chosen performance metric, with the best models at the top of the list. It provides a variety of insight tabs, available based on user permissions and applicability. Hover over an inactive division to view a dropdown of member tabs.
Tabs are visible only if they are applicable to the project type. For example, time series-related tabs (e.g., Accuracy Over Time) only display for time series projects. Tabs that are applicable to a project but not a particular model type display as grayed out (for example, blender models, due to the nature of their construction, have fewer tab functions available).
The pages within this section provide information on using and interpreting the insights available from the Leaderboard (Models tab). See the Leaderboard reference for information on the badges and components of the Leaderboard as well as functions such as tagging, searching, and exporting data.
|Evaluate: Key plots and statistics for judging model effectiveness
|Accuracy Over Space
|Provides a spatial residual mapping within an individual model.
|Accuracy over Time
|Visualizes how predictions change over time.
|Allows you to manually set model parameters, overriding the DataRobot selections.
|Plots data for the selected backtest and provides SHAP explanations for up to 500 anomalous points.
|Anomaly over Time
|Plots how anomalies occur across the timeline of your data.
|Compares actual data values with predicted data values in multiclass projects. For binary classification projects, use the confusion matrix on the ROC Curve tab.
|Removed. See Feature Effects.
|Provides a visual indicator of how well a model predicts at each forecast distance in the project’s forecast window.
|Forecast vs Actual
|Compares how different predictions behave at different forecast points to different times in the future.
|Depicts how well a model segments the target population and how capable it is of predicting the target.
|Clearly visualizes the predictive performance and validity of a regression model.
|Explores classification, performance, and statistics related to a selected model at any point on the probability scale.
|Provides series-specific information.
|Provides an at-a-glance summary of how well a model performs on different backtests.
|Provides an understanding about training activity, per iteration, for Keras-based models.
|Understand: Explains what drives a model’s predictions
|Visualizes the effect of changes in the value of each feature on the model’s predictions.
|Provides a high-level visualization that identifies which features are most strongly driving model decisions.
|Captures latent features in your data, surfacing and communicating actionable insights and identifying segments in your data for further modeling.
|Illustrates what drives predictions on a row-by-row basis using XEMP or SHAP methodology.
|Displays the most relevant words and short phrases in word cloud format.
|Describe: Model building information and feature details
|Provides a graphical representation of the data preprocessing and parameter settings via blueprint.
|Provides, for select models, a visual representation of the most important variables and a coefficient export capability.
|Forces certain XGBoost models to learn only monotonic (always increasing or always decreasing) relationships between specific features and the target.
|Data Quality Handling Report
|Provides transformation and imputation information for blueprints.
|Provides access to model blueprints for Eureqa generalized additive models (GAM), regression models, and classification models.
|Lists operation status results.
|Displays model information.
|Provides access to an export of the model’s complete, validated parameters.
|Predict: Access to prediction options
|Creates a deployment and makes predictions or generates a model package.
|Provides export of a model binary file, validated Java Scoring Code for a model, or charts.
|Makes in-app predictions.
|Compliance: Compiles model documentation for regulatory validation
|Generates individualized model documentation.
|Allows you to create, edit, and share custom documentation templates.
|Comments: Adds comments to a modeling project
|Adds comments to items in the AI Catalog.
|Bias and Fairness: Tests models for bias
|Identifies if a model is biased, and if so, how much and who it's biased towards or against.
|Cross-Class Data Disparity
|Depicts why a model is biased, and where in the training data it learned that bias from.
|Measures the model's accuracy for each class segment of the protected feature.
|Insights and more: Graphical representations of model details
|Visualizes areas of images that a model is using when making predictions.
|Lists the most anomalous rows (those with the highest scores) from the Training data.
|Visualizes relevancy of a collection of categories from summarized categorical features.
|Indicates predictive performance.
|Displays a projection of images onto a two-dimensional space defined by similarity.
|Visualizes relevancy of words and short phrases.
|Tree-based Variable Importance
|Ranks the most important variables in a model.
|Illustrates the magnitude and direction of a feature's effect on a model's predictions.
|Visualizes variable keyword relevancy.
|Helps to determine whether it is worthwhile to increase dataset size.
|Speed vs Accuracy
|Illustrates the tradeoff between runtime and predictive accuracy.
|Compares selected models by varying criteria.
|Bias vs Accuracy
|Illustrates the tradeoff between predictive accuracy and fairness.