Linear Regression VAL

Linear Regression is one of the fundamental types of predictive modeling algorithms. In linear regression, a dependent numeric variable is expressed in terms of the sum of one or more independent numeric variables, which are each multiplied by a numeric coefficient, usually with a constant term added to the sum of independent variables.

Options

Backward Only: Specifies whether to use only backward technique or not, i.e. a forward step is not performed.
Backward: A backward step consists of computing the Partial F-Statistic for each variable and removing that with the smallest value if it is less than the criterion to remove.
Input columns: Specifies the name(s) of the column(s) representing the independent variables used in building a linear regression model.
Condition Index Threshold: Specifies the condition index threshold value to use while generating near dependency report.
Constant: Specifies whether the linear model includes a constant term or not.
Response Column: Specifies the name of the column that represents the dependent variable.
Entrance Criterion: Specifies the criterion to enter a variable into the model. The Partial F-Statistic must be greater than this value, or the T-Statistic P-value must be less than this value.
Forward Only: Specifies whether to use only forward technique or not, i.e. a backward step is not performed.
Forward: A forward step is made by determining the largest partial F-Statistic and adding the corresponding variable to the model.
Group By columns: Specifies the name(s) of the column(s) dividing the input into partitions, one for each combination of values in the group by columns.
Matrix Input: Specifies whether the input teradataml DataFrame passed to argument 'data' represents an ESSCP matrix build by Matrix Building function or not.
XML Report: Specifies whether to produce an XML report showing columns that may be collinear as part of the output or not.
Remove Criterion: Specifies the criterion to remove a variable from the model. The Partial F-Statistic must be less than this value, or the T-Statistic P-value must be greater than this value.
Stats Report: Specifies whether to produce an additional data quality report which includes the mean and standard deviation of each model variable, derived from an ESSCP matrix.
Stepwise: Specifies whether to perform a stepwise procedure or not.
F-Statistic: Specifies whether to use the partial F-Statistic in assessing whether a variable should be added or removed.
T-Statistic P-value: Specifies whether to use the T-Statistic P-value in assessing whether a variable should be added or removed.
Variance Proportion Threshold: Specifies the variance proportion threshold value to use while generating near dependency report.
Output Schema: Output Schema, if Volatile is true then use user login as the schema.
Output Table: Output Table
VAL Location: VAL Location

Linear Regression VAL

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