Evaluate experiments¶

Once you start modeling, Workbench begins to construct your model Leaderboard. The Leaderboard is a ranked list of models that provides a summary of information, including scoring information, for each model built in an experiment.

Once you start modeling, Workbench begins to construct your model Leaderboard, a list of models ranked by performance, to help with quick model evaluation. The Leaderboard provides a summary of information, including scoring information, for each model built in an experiment. From the Leaderboard, you can click a model to access visualizations for further exploration. Using these tools can help to assess what to do in your next experiment.

After Workbench completes Quick mode on the 64% sample size phase, the most accurate model is selected and trained on 100% of the data. That model is marked with the Prepared for Deployment badge.

Manage the Leaderboard¶

There are several controls available, described in the next sections, for navigating the Leaderboard.

• View experiment info
• Filter models
• Sort models by
• Controls

View experiment info¶

Click View experiment info to view a summary of information about the experiment. These are the parameters used to build the models on this Leaderboard.

Filter models¶

Filtering makes viewing and focusing on relevant models easier. Click Filter models to set the criteria for the models that Workbench displays on the Leaderboard. The choices available for each filter are dependent on the experiment and/or model type—they were used in at least one Leaderboard model—and will potentially change as models are added to the experiment. For example:

Sort models by¶

By default, the Leaderboard sorts models based on the score of the validation partition, using the selected optimization metric. You can, however, use the Sort models by control to change the basis of the display parameter when evaluating models.

Note that although Workbench built the project using the most appropriate metric for your data, it computes many applicable metrics on each of the models. After the build completes, you can redisplay the Leaderboard listing based on a different metric. It will not change any values within the models, it will simply reorder the model listing based on their performance on this alternate metric.

See the page on optimization metrics for detailed information on each.

Controls¶

Workbench provides simple, quick shorthand controls:

Icon	Action
	Reruns Quick mode with a different feature list If you select a feature list that has already been run, Workbench will replace and deleted models or make no changes.
	Duplicates the experiment, with an option to reuse just the dataset, or the dataset and settings.
	Deletes the experiment and its models. If the experiment is being used by an application, you cannot delete it.
	Slides the Leaderboard panel closed to make additional room for, for example, viewing insights.

Insights¶

Model insights help to interpret, explain, and validate what drives a model’s predictions. Available insights are dependent on experiment type, but may include:

• Feature Impact
• ROC Curve
• Lift Chart
• Residuals
• Accuracy Over Time
• Stability

To see a model's insights, click on the model in the left-pane Leaderboard.

Feature Impact¶

Feature Impact provides a high-level visualization that identifies which features are most strongly driving model decisions. It is available for all model types and is an on-demand feature, meaning that for all but models prepared for deployment, you must initiate a calculation to see the results.

ROC Curve¶

For classification experiments, the ROC Curve tab provides the following tools for exploring classification, performance, and statistics related to a selected model at any point on the probability scale:

• An ROC Curve
• Cumulative charts
• A confusion matrix
• A payoff matrix/profit curve
• Metrics

Lift Chart¶

To help visualize model effectiveness, the Lift Chart depicts how well a model segments the target population and how well the model performs for different ranges of values of the target variable.

Residuals¶

For regression experiments, the Residuals tab helps to clearly understand a model's predictive performance and validity. It allows you to gauge how linearly your models scale relative to the actual values of the dataset used. It provides multiple scatter plots and a histogram to assist your residual analysis:

• Predicted vs. Actual
• Residual vs. Actual
• Residual vs. Predicted
• Residuals histogram

Accuracy Over Time¶

For time-aware projects, Accuracy Over Time helps to visualize how predictions change over time. By default, the view shows predicted and actual vs. time values for the training and validation data of the most recent (first) backtest. This is the backtest model DataRobot uses to deploy and make predictions. (In other words, the model used to generate the error metric for the validation set.)

The visualization also has a time-aware Residuals tab that plots the difference between actual and predicted values. It helps to visualize whether there is an unexplained trend in your data that the model did not account for and how the model errors change over time.

Stability¶

The Stability tab provides an at-a-glance summary of how well a model performs on different backtests. It helps to measure performance and gives an indication of how long a model can be in production (how long it is "stable") before needing retraining. The values in the chart represent the validation scores for each backtest and the holdout.

Train on new settings¶

Once the Leaderboard is populated, you can retrain an existing model to create a new Leaderboard model. To retrain a model:

1. Select a model from the Leaderboard by clicking on it.

2. Change a model characteristic by clicking the pencil icon (). You can change either the feature list or training data.

   a. Select a new feature list. You cannot change the feature list for the model prepared for deployment because it is a "frozen". (See the FAQ for a workaround to add feature lists.)

   b. Change the sample size (non-time aware) or training period (if date/time partitioning was used). The resulting window depends on the partitioning method.

Once you have set a new value, click Train new models. DataRobot builds the new model and displays it on the Leaderboard.

Compliance documentation¶

DataRobot automates many critical compliance tasks associated with developing a model and, by doing so, decreases time-to-deployment in highly regulated industries. You can generate, for each model, individualized documentation to provide comprehensive guidance on what constitutes effective model risk management. Then, you can download the report as an editable Microsoft Word document (.docx). The generated report includes the appropriate level of information and transparency necessitated by regulatory compliance demands.

The model compliance report is not prescriptive in format and content, but rather serves as a guide in creating sufficiently rigorous model development, implementation, and use documentation. The documentation provides evidence to show that the components of the model work as intended, the model is appropriate for its intended business purpose, and it is conceptually sound. As such, the report can help with completing the Federal Reserve System's SR 11-7: Guidance on Model Risk Management.

To generate a compliance report:

1. Select a model from the Leaderboard.

2. From the Model actions dropdown, select Generate compliance report.

   

3. Workbench prompts for a download location and, once selected, generates the report in the background as you continue experimenting.

Manage experiments¶

At any point after models have been built, you can manage an individual experiment from within its Use Case. Click on the three dots to the right of the experiment name to delete it. To share the experiment, use the Use Case Manage members tool to share the experiment and other associated assets.