TD_GLMPredict function predicts target values (regression) and class labels (classification) for test data using a GLM model of the TD_GLM function.
Before using the features in the function, you must standardize the input features using TD_ScaleFit and TD_ScaleTransform functions.
TD_GLMPredict only accepts numeric features. Before training, you must convert the categorical features to numeric values, such as using:
You can use TD_RegressionEvaluator, TD_ClassificationEvaluator, or TD_ROCTD_ROC function as a post-processing step for evaluating prediction results.
Any observation with missing value (null) in an input column is ignored, and appears in the output with an error code. You can use an imputation function, such as TD_SimpleImputeFit and TD_SimpleImputeTransform to do imputation or fill in the missing values.