Training and Testing Sets
When training a machine learning model, it is common practice to split your dataset into two sets. Training and testing sets. One is used to estimate the parameters for the model while the other one is used to estimate the accuracy of the model(I think it is quite obvious which is which).
The main purpose of this practice is to ensure that the model that comes out of the training is able to generalize into unseen data.