In a machine learning model, the training set is a set of data that is used to train the model, i.e., to adjust the model's parameters in order to minimize the error on the training data. The test set, on the other hand, is a separate set of data that is used to evaluate the performance of the model after it has been trained.

The purpose of the test set is to provide an unbiased estimate of the model's performance on new, unseen data. It is important to use a separate test set to evaluate the model, rather than using the training data, because the model is likely to perform well on the training data due to overfitting. Overfitting occurs when the model is too closely fitted to the training data, resulting in poor generalization to new, unseen data.

The size of the training, validation, and test sets can depend on a number of factors, such as the size of the dataset, the complexity of the model, and the computational resources available. In general, it is recommended to allocate a larger proportion of the dataset to the training set, as the model needs a sufficient amount of data to learn from in order to generalize well to new data.

A common split for the training, validation, and test sets is 70/15/15, with 70% of the data allocated to the training set, 15% allocated to the validation set, and 15% allocated to the test set. The validation set can be used to tune the model's hyperparameters, such as the learning rate or the number of hidden layers, while the test set is used to evaluate the final performance of the model. However, the specific split between the training, validation, and test sets can vary depending on the specific needs of the project.

There is a difference between training Set and test Set. Training data is the subset of original data that is used to train the machine learning model, whereas testing data is used to check the accuracy of the model. The training dataset is generally larger in size compared to the testing dataset