# Make predictions

> Make predictions - After you create an experiment and train models, you can provide scoring data,
> make predictions, and download the results.

This Markdown file sits beside the HTML page at the same path (with a `.md` suffix). It summarizes the topic and lists links for tools and LLM context.

Companion generated at `2026-05-06T18:17:10.067232+00:00` (UTC).

## Primary page

- [Make predictions](https://docs.datarobot.com/en/docs/workbench/nxt-workbench/experiments/make-predictions.html): Full documentation for this topic (HTML).

## Sections on this page

- [Set time series options](https://docs.datarobot.com/en/docs/workbench/nxt-workbench/experiments/make-predictions.html#set-time-series-options): In-page section heading.
- [Set prediction options](https://docs.datarobot.com/en/docs/workbench/nxt-workbench/experiments/make-predictions.html#set-prediction-options): In-page section heading.
- [Compute and download predictions](https://docs.datarobot.com/en/docs/workbench/nxt-workbench/experiments/make-predictions.html#compute-and-download-predictions): In-page section heading.

## Related documentation

- [NextGen UI documentation](https://docs.datarobot.com/en/docs/workbench/index.html): Linked from this page.
- [Workbench](https://docs.datarobot.com/en/docs/workbench/nxt-workbench/index.html): Linked from this page.
- [Predictive experiments](https://docs.datarobot.com/en/docs/workbench/nxt-workbench/experiments/index.html): Linked from this page.
- [wrangled](https://docs.datarobot.com/en/docs/workbench/nxt-workbench/dataprep/wrangle-data/index.html): Linked from this page.
- [running EDA](https://docs.datarobot.com/en/docs/reference/data-ref/eda-explained.html): Linked from this page.
- [Snowflake data connection](https://docs.datarobot.com/en/docs/workbench/nxt-workbench/dataprep/add-data/connect.html): Linked from this page.
- [Limits](https://docs.datarobot.com/en/docs/api/reference/batch-prediction-api/index.html#limits): Linked from this page.
- [requirements for the scoring dataset](https://docs.datarobot.com/en/docs/api/reference/batch-prediction-api/batch-pred-ts.html#requirements-for-the-scoring-dataset): Linked from this page.
- [Prediction Explanations](https://docs.datarobot.com/en/docs/classic-ui/modeling/analyze-models/understand/pred-explain/index.html): Linked from this page.
- [ngram](https://docs.datarobot.com/en/docs/reference/glossary/index.html#n-gram): Linked from this page.
- [include pre-computed prediction intervals when registering the model package](https://docs.datarobot.com/en/docs/workbench/nxt-registry/nxt-model-directory/nxt-register-dr-models.html): Linked from this page.
- [enabling prediction intervals](https://docs.datarobot.com/en/docs/workbench/nxt-console/nxt-predictions/nxt-prediction-intervals.html): Linked from this page.

## Documentation content

After you create an [experiment](https://docs.datarobot.com/en/docs/workbench/nxt-workbench/experiments/index.html) and train models, you can make predictions on new data, registered data, or training data to validate those models.

To make predictions with a model in a Workbench experiment:

1. Select the model from theModelslist and then clickModel actions>Make predictions.
2. On theMake Predictionspage, upload aPrediction source, drag a file into thePrediction datasetbox, or clickChoose fileand select one of the following: Upload methodDescriptionUpload local fileSelect a file from your local filesystem to upload that dataset for predictions.Use model training dataSelect a portion of the training data to use as a prediction dataset.Data RegistrySelect a file previously uploaded to the Data Registry.Wrangler recipeSelect a recipewrangledin Workbench from a Snowflake data connection or Data Registry dataset. Upload local fileUse model training dataData registryWrangler recipeIn your local filesystem, select a dataset file, and then clickOpen.When you upload a prediction dataset, it is automatically stored in theAI Catalogonce the upload is complete. Be sure not to navigate away from the page during the upload, or the dataset will not be stored in the catalog. If the dataset is still processing after the upload, that means DataRobot isrunning EDAon the dataset before it becomes available for use.Select one of the following options, depending on the project type:Project typeOptionsAutoMLSelect one of the following training data options:ValidationHoldoutAll dataOTV/Time SeriesSelect one of the following training data options:All backtestsHoldoutIn-sample prediction riskDepending on the option you select and the sample size the model was trained on, predicting on training data can generate in-sample predictions, meaning that the model has seen the target value during training and its predictions do not necessarily generalize well. If DataRobot determines that one or more training rows are used for predictions, theOverfitting riskwarning appears. These predictions should not be used to evaluate the model's accuracy.In theSelect a datasetpanel, click a dataset, and then clickConfirm.In theSelect recipepanel, select the checkbox for a recipewrangledfrom aSnowflake data connectionor from the Data Registry, and then clickSelect.Filter and review recipesTo filter the list of wrangled recipes by source, click theSourcesfilter, and selectSnowflakeorData Registry. To learn more about a recipe before selecting it, click the recipe row to view basic information and the wrangling SQL query, or, clickPreviewafter selecting the recipe from the list. Time series data requirementsMaking predictions withtime series models requires the dataset to be in a particular format. The format is based on your time series project settings. Ensure that the prediction dataset includes the correct historical rows, forecast rows, and any features known in advance. In addition, to ensure DataRobot can process your time series data, configure the dataset to meet the following requirements:Sort prediction rows by their timestamps, with the earliest row first.For multiseries, sort prediction rows by series ID and then by timestamp, with the earliest row first.There isno limiton the number of series DataRobot supports. The only limit is the job timeout, as mentioned inLimits. For dataset examples, see therequirements for the scoring dataset If you select the wrong dataset, you can remove your selection from thePrediction sourcesetting by clicking the delete icon ().
3. Next, you canset the prediction options(for time series models, you can alsoset the time series options) and thencompute predictions.

## Set time series options

> [!NOTE] Time series options availability
> If you selected Use model training data as the prediction source, you can't configure the time series options.

After you configure the Prediction source with a [properly formatted time series prediction dataset](https://docs.datarobot.com/en/docs/workbench/nxt-workbench/experiments/make-predictions.html#time-series-data-requirements), you can configure the time series-specific settings in the Time series options section. Under Forecast point, select a Selection method to define the date from which you want to begin making predictions:

- Set automatically: DataRobot selects the latest date that includes a target value and then adds the FDW offset.
- Set manually: Select a forecast point within the date range DataRobot detects from the provided prediction source (for example, "Select a date between2012-07-05and2014-06-20").

In addition, you can click Show advanced options and enable Ignore missing values in known-in-advance columns to make predictions even if the provided source dataset is missing values in the known-in-advance columns; however, this may negatively impact the computed predictions.

## Set prediction options

After you configure the Prediction source, you can configure optional settings in the Prediction options section:

| Setting | Description |
| --- | --- |
| Include additional feature values in prediction results | Include input features (columns) in the prediction results file alongside the predictions, based on the selection option: Add specified features: Filter for and include the selected features from the datasetAdd all features: Include every feature from the dataset. You can only append a feature (column) present in the original dataset, although the feature does not have to have been part of the feature list used to build the model. Derived features are not included. |
| Include Prediction Explanations | Adds columns for Prediction Explanations to your prediction output.Number of explanations: Enter the maximum number of explanations you want to request from the deployed model. You can request 100 explanations per prediction request.Low prediction threshold: Enable and define this threshold to provide prediction explanations for any values below the set threshold value.High prediction threshold: Enable and define this threshold to provide prediction explanations for any values above the set threshold value.Number of ngram explanations: Enable and define the maximum number of text ngram explanations to return per row of the dataset. The default (and recommended) setting is all (no limit). If you can't enable Prediction Explanations, see Why can't I enable Prediction Explanations?. |
| Classes | For multiclass models with Prediction Explanations enabled, control the method for selecting which classes are used in explanation computation.The Classes options include:Predicted: Select classes based on prediction value. For each row in the prediction dataset, compute explanations for the number of classes set by the Number of classes value.Actual: For predictions on the training dataset, compute explanations from classes that are known values. For each row, explain the class that is the "ground truth."List of classes: Select one or more specific classes from a list of classes. For each row, explain only the classes selected in the List of Classes menu. |
| Include prediction intervals | For time series models, include only predictions falling within the specified interval, based on the residual errors measured during the model's backtesting. |

> [!WARNING] Prediction intervals in DataRobot serverless prediction environments
> In a DataRobot serverless prediction environment, to make predictions with time-series prediction intervals included, you must [include pre-computed prediction intervals when registering the model package](https://docs.datarobot.com/en/docs/workbench/nxt-registry/nxt-model-directory/nxt-register-dr-models.html). If you don't pre-compute prediction intervals, the deployment resulting from the registered model doesn't support [enabling prediction intervals](https://docs.datarobot.com/en/docs/workbench/nxt-console/nxt-predictions/nxt-prediction-intervals.html).

**Q: Why can't I enable Prediction Explanations?**

If you can't Include Prediction Explanations, it is likely because:

- The model's validation partition doesn't contain the required number of rows.
- For a Combined Model, at least one segment champion validation partition doesn't contain the required number of rows. To enable Prediction Explanations, manually replace retrained champions before creating a model package or deployment.

## Compute and download predictions

After you configure the Prediction options, click Compute and download predictions to start scoring the data, then view the scoring results under Download recent predictions:

From the Download recent predictions list, you can do the following:

- While the prediction job is running, you can click the close icon () to stop the job.
- If the prediction job is successful, click the download icon () to download a predictions file or the logs icon () to view and optionally copy the run details. NotePredictions are available for download for 48 hours from the time of prediction computation.
- If the prediction job failed, click the logs icon () to view and optionally copy the run details.