- ModelTable
- Specify the name for the output table that contains the trained model. The trained model contains parameters, statistics, and the coefficients of the predictors for lambda. This table must not exist.
- RegularizationTable
- [Optional] Specify the name for the output table that contains the statistics and coefficients of each lambda. Recommended if you want predicted results for each lambda from GLM2Predict.
- InputColumns
- Specify the names of the input_table columns that contain the variables to use as predictors (independent variables).
- CategoricalColumns
- [Optional] Specify the names of the input_table columns that contain categorical variables, and which of their categories to use in the model.
- WeightColumn
- [Optional] Specify the name of the input_table column that contains the weights to assign to responses.
- ResponseColumn
- Specify the name of the input_table column that contains the responses.
- Family
- [Optional] Specify the distribution exponential family.
- Lambda
- [Optional. Disallowed if NumLambdas is specified.] Specify the regularization parameter sequence. Each lambda must be a nonnegative DOUBLE PRECISION value. A value of zero disables regularization.
- NumLambdas
- [Required if Lambda is omitted, disallowed otherwise] Specify the number of lambda values in the regularization parameter sequence. The num_lambdas must be a positive INTEGER. The function uses num_lambdas and min_lambda_ratio to compute the regularization parameter sequence.
- MinLambdaRatio
- [Required if Lambda is omitted, disallowed otherwise] Specify the minimum lambda value in the regularization parameter sequence (MinLambda) as a fraction of the maximum lambda value in the regularization parameter sequence (MaxLambda). The min_lambda_ratio must be in [0, 1).
- StopThreshold
- [Optional] Specify the convergence threshold of coordinate descent. The threshold must be a nonnegative DOUBLE PRECISION value.
- Alpha
- [Optional] Specify the mixing parameter for penalty computation (see the following table). The alpha must be in [0, 1]. If alpha is in (0,1), it represents α in the elastic net regularization formula in GLM2.
alpha Regularization Type Parameter Description 0 Ridge (0,1) Elastic net 1 LASSO - MaxIterNum
- [Optional] Specify the maximum number of iterations over the data for all lambda values. The parameter max_iterations must be a positive INTEGER value.
- Intercept
- [Optional] Specify whether the function uses an intercept. For example, in β0+β1*X1+β2*X2+ ....+ βpXp, the intercept is β0.