On the Applications tab, click Open next to the application you want to launch—from here you can test different application configurations before sharing it with users.
End-users must sign in with a DataRobot account or access the application via a link that can be shared with users outside of DataRobot.
|Application name||Displays the application name. Click to return to the app's Home page.|
|Pages||Navigates between application pages.|
|Build||Allows you to edit the application.|
|Share||Share the application with users, groups, or organizations within DataRobot.|
|Add new row||Opens the Create Prediction page, where you can make single record predictions.|
|Add Data||Upload batch predictions—from the AI Catalog or a local file.|
|All rows||Displays a history of predictions. Select a row to view prediction results for that entry.|
To make multiple prediction requests at once from the Home page, click choose file or drag the files into the box.
After adding new files, the application processes your predictions and displays them in the All Rows widget on the Home page. Click on any record to view the prediction results.
Single record predictions¶
To make a new prediction:
- Click Add new row, bringing you to the Create Prediction page with the Add New Row widget, which displays the features available to make a prediction.
Fill in the feature fields—the only field required for making predictions is the association ID if one was added for the deployment. If a field is left blank, the feature field displays N/A on the prediction results page. Alternatively, you can click Populate averages to fill in the fields with the average value for each feature.
Selecting location features
If the dataset contains a location feature, a globe icon appears in the feature field. You can manually enter a feature value in the field, or click the globe icon to view a visual representation of the training data.
The geometry type of the location feature determines the appearance of the training data on the map and affects which draw tool—Point, Polygon, or Path—you can use to highlight your prediction. In the example below, the location feature uses point geometry, so use the Point tool to add a new point to the map. With the point selected, click Save selected location; the point is then converted to a geojson string to make your prediction.
Click Add. After DataRobot completes the request, the prediction results page opens.
To add or remove feature fields, click Build and navigate to the Create Prediction page.
The prediction results page displays prediction information and insights based on the values entered for an individual prediction. This page automatically opens after making a single record prediction, but you can also view prediction results by selecting a row in the All rows widget.
The General Information widget displays helpful values for features that don't necessarily impact the prediction results, and the Prediction Information widget displays the values for features likely to impact the prediction, as well as Prediction Explanations.
Add or remove fields
To add or remove feature fields, click Build and navigate to the Prediction Details page. Once there, select the widget and click Manage in the Data tab. For more information, see the instructions for configuring chart widgets.
The Prediction Explanations widget displays a chart with your predictions results, as well as a table with Prediction Explanations—a qualitative measure of how features in the model impact a prediction based on their relationship to the target.
|The predicted value, based on the values represented as a percentage.|
|The basis of the prediction classification, determined by the dataset the app deployed from.|
|A table displaying the top 10 Prediction Explanations as well as a visualization representing how the feature is distributed in the training data.|
You must compute Prediction Explanations for the model before making a prediction.
Prediction Explanation table¶
The Prediction Explanation table displays the top 10 features with the largest impact on a prediction based on their relationship in the training data.
See the table below for a description of each field:
|Impact||The measured impact of a given feature on the prediction. For a description of the icons used, see "qualitativeStrength" indicator.|
|Feature||The feature in the training data impacting the prediction.|
|Value||The feature value causing the feature's measured impact on the prediction.|
|Distribution||A histogram that represents the distribution of a given feature in the training data. Hover over the visualization for additional information that can then be used to add context to the Prediction Explanation.|
What-if and Optimizer¶
The What-if and Optimizer widget displays various scenarios in a chart or a table view. To learn how to configure this widget, see the documentation on building applications
|Opens the Add scenario pane.|
|Displays only actual, optimal, and all manually added scenarios.|
|Displays prediction insights in chart or table view.|
|Adds new scenarios to the widget.|
|Selects features for the x- and y-axis.|
Check Show only manually added, optimal, and actual scenarios to more easily find your simulations.
If the What-if functionality is enabled, you can manually add scenarios to the widget's display.
To create a simulation, click Add scenario.
Provide values for each variable selected on the Build page. When you have entered values for each feature, click Add. This triggers a prediction request to a DataRobot prediction server and returns a predicted value for your target feature.
Selecting a point on the chart (data point) opens a pane on the right that displays prediction results and feature values for the selected scenario. If Prediction Explanations are available for your prediction request, they appear in the Prediction Explanations tab in the same pane.
You can continue to make predictions by updating the variable inputs with new values and repeating this process. After making several predictions, click Table view. This view allows you to drag-and-drop scenarios for side-by-side comparison.
Optimization simulation results¶
If the Optimizer functionality is enabled, the chart displays an optimal scenario, as well as different outcomes based on the flexible features added to the widget's configuration.
Once DataRobot completes running simulations, the chart populates the results. The y-axis measures the values of the target feature, and the x-axis indicates the simulation iteration. Each point on the graph represents the predicted value for each simulation run.
The orange data point represents your prediction and the green data point represents the optimal scenario—the values of the feature that most often produce the optimal result for your target (the minimal or maximal, based on your settings). The selected values are those you selected for a given iteration, allowing you to compare the selected values for each iteration to the overall most optimal values determined by the app.