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.
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.

Additional Resources

Snap History

Snap Pack History

Click to view/expand

Release	Snap Pack Version	Date	Type	Updates
November 2024	main29029	13 Nov 2024	Stable	Updated and certified against the current SnapLogic Platform release.
August 2024	main27765	21 Aug 2024	Stable	Upgraded the `org.json.json` library from v20090211 to v20240303, which is fully backward compatible.
May 2024	main26341	08 May 2024	Stable	Updated and certified against the current SnapLogic Platform release.
February 2024	main25112	14 Feb 2024	Stable	Updated and certified against the current SnapLogic Platform release.
November 2023	main23721	Nov 8, 2023	Stable	Updated and certified against the current SnapLogic Platform release.
August 2023	main22460	Aug 16, 2023	Stable	Updated and certified against the current SnapLogic Platform release.
May 2023	433patches21854	14 Jul 2023	Latest	Fixed an issue with the Cross Validator (Classification) Snap where the native Windows DLL caused the Snaplex to stall
May 2023	433patches21644	28 Jun 2023	Latest	Improved an error message in the Remote Python Script Snap to explain the reason and resolution for the case where a Python script has errors.
May 2023	main21015	10 May 2023	Stable	Upgraded with the latest SnapLogic Platform release.
February 2023	main19844	09 Feb 2023	Stable	Upgraded with the latest SnapLogic Platform release.
November 2022	main18944	10 Nov 2022	Stable	Upgraded with the latest SnapLogic Platform release.
August 2022	main17386	11 Aug 2022	Stable	Upgraded with the latest SnapLogic Platform release.
4.29	429patches16809	20 Jul 2022	Latest	Removed the log4j dependency from the ML Core Snaps due to security vulnerabilities.
4.29	main15993	14 May 2022	Stable	Upgraded with the latest SnapLogic Platform release.
4.28	main14627	20 Jul 2022	Stable	Upgraded with the latest SnapLogic Platform release.
4.27	427patches13948	07 Jan 2022	Latest	Fixed an issue with the following Snaps, where a deadlock occurred when data is loaded from both the input views. Predictor Classification Predictor Regression Clustering
4.27	main12833	13 Nov 2021	Stable	Upgraded with the latest SnapLogic Platform release.
4.26	main11181	14 Aug 2021	Stable	Upgraded with the latest SnapLogic Platform release.
4.25	main9554	08 May 2021	Stable	Upgraded with the latest SnapLogic Platform release.
4.24	main8556	13 Feb 2021	Stable	Upgraded with the latest SnapLogic Platform release.
4.23	main7430	14 Nov 2020	Stable	Upgraded with the latest SnapLogic Platform release.
4.22	main6403	12 Sep 2020	Stable	Upgraded with the latest SnapLogic Platform release.
4.21	snapsmrc542	09 May 2020	Stable	Upgraded with the latest SnapLogic Platform release.
4.20 Patch	mlcore8770	18 Mar 2020	Stable	Adds the log4j dependency to the ML Core Snaps to resolve the "`Could not initialize class org.apache.log4j.LogManage`r" error.
4.20	snapsmrc535	08 Feb 2020	Stable	Upgraded with the latest SnapLogic Platform release.
4.19	snapsmrc528	14 Nov 2019	Stable	Upgraded with the latest SnapLogic Platform release.
4.18	snapsmrc523	10 Aug 2019	Stable	Upgraded with the latest SnapLogic Platform release.
4.17 Patch	ALL7402	11 Jun 2019	Latest	Pushed automatic rebuild of the latest version of each Snap Pack to SnapLogic UAT and Elastic servers.
4.17	snapsmrc515	11 Jun 2019	Latest	New Snap: Introducing the Clustering Snap that performs exploratory data analysis to find hidden patterns or groupings in data. Enhanced the AutoML Snap. You can now: Select algorithms to derive the top models. Input the best model generated by another AutoML Snap from a previous execution. View an interactive HTML report that contains statistics of up to 10 models. Added the Snap Execution field to all Standard-mode Snaps. In some Snaps, this field replaces the existing Execute during preview check box.
4.16	snapsmrc508	16 Feb 2019	Stable	New Snap: Introducing the AutoML Snap, which lets you automate the process of selecting machine learning algorithms and tuning hyperparameters. This Snap gives the best predictive model within the specified time limit.
4.15	snapsmrc500	15 Dec 2018	Stable	New Snap Pack. Perform data modeling operations such as model training, cross-validation, and model-based predictions. Additionally, you can also execute Python scripts remotely. Snaps in this Snap Pack are: Cross Validator -- Classification Cross Validator -- Regression Predictor -- Classification Predictor -- Regression Remote Python Script Trainer -- Classification Trainer -- Regression Releases the Remote Python Executor account and the Remote Python Executor Dynamic account for the Remote Python Script Snap.

Predictor -- Regression