Inputs for Linear and Non-Linear Kernels
TD_OneClassSVMPredict accepts two inputs for linear kernel and three outputs for non-linear RBF kernel.
- InputTable: Contains the test data set that needs to be predicted. The function predicts each row independent of the other rows based on the model trained by TD_OneClassSVM. The preprocessing steps carried out for TD_OneClassSVM should be done for the test data set as well before prediction.
- ModelTable: Contains the model trained by TD_OneClassSVM. Manually modifying this table can return erroneous results. TD_OneClassSVMPredict only reads the weights and Loss function information from this table, and not the rest of the metrics.
- FitTable: This table is required if the 'Kernel' argument is 'RBF'. Manually modifying this table can return erroneous results. TD_OneClassSVM uses this table to transform the input dataset before creating the model. TD_OneClassSVMPredict should use the same weights and bias from this table to transform the test data set for correct results.