In a dataset, a training set is used to create ML models. While in a test set, the response of the models are checked whether it has the desired accuracy. Data that’s fed into the training set is usually excluded from the data in the test sets to check whether the output has more sources of information or not. Another important point to note is that there is no specific proportion to the data inputs and outputs. Usually, it is thought that if you give 70% training data, you expect 30% test data. However, the input datas are gradually decreased to find out whether the test datas can give better outputs on its own research and abilities to find new corresponding datas.