R-Linear Regression

Properties that can be configured for the R-Linear Regression algorithm.

Syntax Use this algorithm to find trends in data. This algorithm performs univariate regression analysis. It determines how an individual variable influences another variable by using the R open-source library.
Note The data type of columns used during model scoring should be same as the data type of columns used while building the model.
R-Linear Regression Properties
Table 1: Algorithm Properties
Property Description
Output Mode Select the mode in which you want to use the output of this algorithm.
Possible values:
  • Fill: Fills missing values in the target column.
  • Trend: Predicts the values for the dependent column and adds an extra column in the output containing the predicted values.
Independent Column Select the input column with which you want to perform the regression analysis.
Dependent Column Select the target column for which you want to perform the regression analysis.
Missing Values Select the method for handling missing values.
Possible methods:
  • Ignore: The algorithm skips the records containing missing values in the independent or dependent columns.
  • Keep: The algorithm retains the records containing missing values during calculation.
  • Stop: The algorithm stops the execution if a value is missing in the independent column or the dependent column.
Allow Singular Fit A Boolean value - if set to true, the aliased coefficients are ignored in the coefficient covariance matrix. If set to false, a model with aliased coefficients produces an error.

A model with aliased coefficients signifies that the square matrix x*x is singular.

Contrasts Select the list of contrasts, which you want to use for factors appearing as variables in the model.
Predicted Column Name Enter a name for the newly-created column that contains the predicted values.