Training and test sets in machine learning refer to the two subsets of data that are used in the development of machine learning algorithms. The training set is a dataset used to train the machine learning model, while the test set is a dataset used to evaluate the generalization performance of the trained model. The training set is typically used to adjust parameters of the model, such as the weights and the bias, so that the model accurately describes the data in the training set. The test set is used to evaluate the accuracy of the model predictions on unseen data, using metrics such as precision, recall, and F1 score.
The use of training and test sets in machine learning is essential in developing accurate and robust models since the performance of a model on data that it has seen before is usually not indicative of its true performance when dealing with unseen data. It is recommended to use a combination of both sets for different purposes. For example, creating a validation set that is used to tune hyperparameters and compare different algorithms, and a test set to evaluate the performance of the final model.
In general, the training and test sets should be divided randomly such that the data is roughly evenly distributed between the two sets. Moreover, it is important to ensure that the training and test sets are representative of the data used in practical applications, as over-fitting the training set may lead to poor performance in the test set.
