The output table schema depends on whether you specify the regularization table and Lambda argument.
Output Table Schema, Regularization Table or Lambda Omitted
If you omit the regularization table, or specify the regularization table but omit the Lambda argument, the output table has the following schema.
Column | Data Type | Description |
---|---|---|
accumulate_column | Same as in input table | [Column appears once for each specified accumulate_column.] Column copied from input table. |
prediction | DOUBLE PRECISION | Score of input data, given by equation g-1(Xβ), where g-1 is the inverse link function, X is the predictors, and β is the vector of coefficients estimated by the GLM2 function. For BINOMIAL classification, a predicted value close to 1 indicates a high probability of class 1. A predicted value close to 0 indicates a high probability of class 0. For other values of Family, scores are expected values of dependent/response variable, conditional on predictors. |
lambda | DOUBLE PRECISION | Lambda value used for prediction. If you omit both the regularization table and Lambda argument, this value is the minLambda value from the model table. |
Output Table Schema, Regularization Table and Lambda Specified
Column | Data Type | Description |
---|---|---|
accumulate_column | Same as in input table | [Column appears once for each specified accumulate_column.] Column copied from input table. |
prediction | DOUBLE PRECISION | Score of input data, given by equation g-1(Xβ), where g-1 is the inverse link function, X is the predictors, and β is the vector of coefficients estimated by the GLM2 function. For BINOMIAL classification, a predicted value close to 1 indicates a high probability of class 1. A predicted value close to 0 indicates a high probability of class 0. For other values of Family, scores are expected values of dependent/response variable, conditional on predictors. |
reqlambda | DOUBLE PRECISION | Lambda value that you specified. |
matchedlambda | DOUBLE PRECISION | Lambda value used for prediction. |