The TD_TrainTestSplit function simulates how a model would perform on new data. The function randomly divides the data sets into train and test subsets to evaluate machine learning algorithms and validation processes. The first subset is used to train the model. The second subset is used to make predictions and compare the predictions to actual values. The proportion of the data allocated to the training set and the testing set can vary, but a common split is 75% of the data for training and 25% for testing.
The TD_TrainTestSplit function gives consistent results across multiple runs on same machine. With different machines, it might produce different train and test datasets.