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.
You can use TD_RegressionEvaluator, TD_ClassificationEvaluator, or TD_ROCTD_ROC function as a post-processing step for evaluating prediction results.
GLM prediction involves using a fitted model to make predictions about the response variable for new observations. Given the predictor variables for a new observation, the linear predictor is calculated using the coefficients estimated from the model. This linear predictor is then transformed using the link function to obtain an estimate of the mean of the response variable for the new observation.
The probability distribution assumed by GLM is then used to obtain a prediction interval for the response variable, which describes the range of values that the response variable is likely to fall within with a specified level of confidence. The width of the prediction interval depends on the variability of the response distribution and the uncertainty in the estimated model coefficients.
TD_GLMPredict can be used for a variety of tasks, such as predicting the probability of an event (binary classification), predicting the count of events (Poisson regression), or predicting a continuous outcome variable (linear regression).
The accuracy of GLM predictions are evaluated using measures such as the mean squared error or the proportion of correctly classified cases. It is important to assess the predictive performance of a GLM using a separate test dataset, as overfitting can occur when using the same data for both model fitting and prediction.