Outlier Detection

Outlier Detection

Outlier Detection is located under Model Studio (  ) in Data Preparation, in the task pane on the left. Use drag-and-drop method to use algorithm in the canvas. Click the algorithm to view and select different properties for analysis.

Refer to Properties of Outlier Detection.


Properties of Outlier Detection

The available properties of Outlier Detection are as shown in the figure given below.


The table given below describes different fields present on Properties of Outlier Detection.

Field

Description

Remark

RunIt allows you to run the node.-
ExploreIt allows you to explore the successfully executed node.-
Vertical Ellipses

The available options are

  • Run till node
  • Run from node
  • Publish as a model
  • Publish code
-

Task Name

It displays the name of the selected task.

You can click the text field to edit or modify the name of the task as required.

Columns

It displays a list of columns in the dataset.

You can select the column names to detect the outliers in those columns.

Group By

It allows you to select the field you want to group by based on the results.

  • Only one field can be selected.
  • It groups the results based on the selected field.

Advanced

It displays advance options for outlier detection.

—

Method for Outlier Detection

It allows you to choose the outlier detection method.

Available options are:

Remove Outliers from Output Data

It allows you to choose the option to remove outliers from the dataset.

Available options are:

  • No – Do not remove outliers.
  • Both sides – Remove outliers on both upper as well as lower side.
  • Only lower – Remove outliers only from the lower side.
  • Only upper – Remove outliers only from the upper side.

Outliers to be Treated

It allows you to choose the option to treat the outliers with a method of your choice.

Available options are:

  • Both sides – Treat outliers on both sides.
  • Only lower – Treat only lower side outliers.
  • Only upper – Treat only upper side outliers.
  • No – Do not treat the outliers. Leave them as is.

Method for Outlier Correction

It allows you to choose the method to correct the outliers in your dataset.

Available options are:





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