Workbench in 5¶

Building models in DataRobot—regardless of the data handling, modeling options, prediction methods, and deployment actions—comes down to the same five basic actions. Review the steps below to get a quick understanding of how to be successful in DataRobot.

Datasets for testing¶

You can use the following hospital readmissions demo datasets to try out DataRobot functionality:

Download training data Download scoring data

These sample datasets are provided by BioMed Research International to study readmissions across 70,000 inpatients with diabetes. The researchers of the study collected this data from the Health Facts database provided by Cerner Corporation, which is a collection of clinical records across providers in the United States. Health Facts allows organizations that use Cerner’s electronic health system to voluntarily make their data available for research purposes. All the data was cleansed of PII in compliance with HIPAA.

1: Create a Use Case¶

From the Workbench directory, click Create Use Case in the upper right:

Provide a name for the use case and click the check mark to accept. You can change this name at any time by opening the Use Case and clicking on the existing name:

2: Work with data¶

Working with data involves importing (or connecting), exploring, and preparing your data. Three steps and your data is ready for modeling.

3: Build experiments¶

Within a Use Case, build experiments using the data you have added and then start modeling. Each Workbench experiment is a set of parameters (data, targets, and modeling settings) that you can compare to find the optimal models to solve your business problem. First, add a new experiment:

Next, add data to the new experiment by selecting the dataset that you just loaded to the Use Case.

Select a target and start modeling.

4: Evaluate models¶

DataRobot's Leaderboard shows you all the models built for your experiment, ranked by performance to help with quick model evaluation.

From the Leaderboard, create new feature lists or click a model to access visualizations for further exploration. These tools interpret, explain, and validate what drives a model’s predictions, and can help inform what to do in your next experiment.

You can also generate compliance documentation from the Leaderbaord.

5: Make predictions¶

Once you have selected a model, you can make predictions with it to assess model performance before deploying. Select the model from the Leaderboard and then click Model actions > Make predictions.

On the Make Predictions page, upload a Prediction source:

After you upload a prediction source, you can configure the prediction options and compute predictions.

Next steps¶

After you have built a model—and made predictions to test accuracy—transfer to DataRobot Classic, find your experiment in the project management center, and deploy the model from the Leaderboard.

From here, you can also:

• Create DataRobot Notebooks.
• Leverage AI Accelerators.