Regression Evaluator (DB)

Computes several commonly used statistical tests to determine the accuracy of several columns (Predicted Values). These represent predictions against one column (Actual Value), which is specified as the "ground truth."

Information at a Glance

Category	Model Validation
Data source type	DB
Sends output to other operators	No
Data processing tool	n/a

Note: The Regression Evaluator (DB) operator is for database data only. For Hadoop data, use the Regression Evaluator (HD) operator.

For information about the metrics used in this operator, see Computed Metrics and Use Case for the Regression Evaluator.

Input

A tabular data set from a database that contains a numeric column of actual values (known truth) and numeric column(s) of predicted values.

Configuration

Parameter	Description
Notes	Any notes or helpful information about this operator's parameter settings. When you enter content in the Notes field, a yellow asterisk is displayed on the operator.
Actual Value	A numeric column that holds the dependent variable that the models were used to train on, or a column of known values for the dependent variable.
Predicted Values (to Compare with Actual Value)	A set of numeric column(s) whose results predict the model. For example, if you are using this to evaluate several different linear regressions, the predicted values for each of the regressions is selected here.
Output Type	TABLE outputs a database table. Specifying TABLE enables Storage Parameters. VIEW outputs a database view.
Output Schema	The schema for the output table or view.
Output Table	The table path and name where the results are output. By default, this is a unique table name based on your user ID, workflow ID, and operator.
Drop If Exists	Specifies whether to overwrite an existing table. Yes - If a table with the name exists, it is dropped before storing the results. No - If a table with the name exists, the results window shows an error message.