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Overview
This is a Transform type Snap that enables you to predict the target field for an unlabeled dataset. An unlabeled dataset is one that does not have a regression field. So the Snap reads this unlabeled dataset and predicts the regression field, which is the target field. Predictions are made based on a regression model. You can build the regression model using the Trainer (Regression) Snap.
Input and Output
Expected input: An unlabeled dataset and the regression model for predicting the regression data.
Expected output: Predictions from the regression model based on the input dataset.
Expected upstream Snaps:
- First input view: Any Snap that generates an unlabeled dataset document is usable as an upstream Snap. For example, CSV Generator, JSON Generator, XML Generator, Copy, and so on.
- Second input view: Any Snap that reads and outputs the regression model. For example, File Reader.
Expected downstream Snaps: CSV/JSON Formatter Snap and File Writer Snap can be used to write the output to file.
Prerequisites
- The input dataset must be in tabular format (no nested structure).
- This Snap automatically derives the schema (field names and types) from the first row. Therefore, the first row must not have any missing values.
Configuring Accounts
Accounts are not used with this Snap.
Configuring Views
Input | This Snap has exactly two document input views. The first input view is for the unlabeled dataset that requires prediction. The second input view is for the regression model to be used for the prediction. |
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| Output | This Snap has exactly one document output view. |
| Error | This Snap has at most one document error view. |
Troubleshooting
None.
Limitations and Known Issues
None.
Modes
- Ultra pipelines: Works in Ultra pipelines.
- Spark mode: Does not work in Spark mode.
Snap Settings
| Label | Required. The name for the Snap. Modify this to be more specific, especially if there are more than one of the same Snap in the pipeline. |
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Additional Resources
Snap History
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