| Argument | Category | Description |
|---|---|---|
| InputTable | Required | Specifies the name of the input table. |
| OutputTable | Required | Specifies the name of the output table. |
| InputColumns | Required | Specifies the names of the columns of the input table that contain the response and predictors. The syntax of predictor_columns is: {col[,...] | [start_column:end_column]}[,...]
where col is a column name and start_column and end_column are the column indexes of the first and last columns in a range of columns. The range includes start_column and end_column. The leftmost column has column index 0, the column to its immediate right has column index 1, and so on.
|
| Method | Optional | Specifies the method to use for linear regression. The default value is 'lasso'. |
| Intercept | Optional | Specifies whether an intercept is included in the model (and not penalized). The default value is 'true'. |
| Normalize | Optional | Specifies whether each predictor is standardized to have unit L2 norm. The default value is 'true'. |
| MaxIterNum | Optional | Specifies the maximum number of steps the function executes. The default value is 8*min(number_of_predictors, sample_size - intercept). For example, if the number of predictors is 11, the sample size (number of rows in the input table) is 1532, and the intercept is 1, then the default value is 8*min(11, 1532 - 1) = 88. |