In a dataset a training set is implemented to build up a model, while a test (or validation) set is to validate the model built. Data points in the training set are excluded from the test (validation) set

Explanation:

These lines are in reference to a question which is;

What is the difference between training and test dataset?

As the name suggests, a training data set is defined to be used on a model. This is done in machine learning programming where training data makes up the model and then a test dataset is defined to determine the functionality or the output of the model when given test data.

The proportion of training data and test data is upto programmer, but usually training dataset is more than test dataset

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