[machine-learning] How to interpret "loss" and "accuracy" for a machine learning model

Just to clarify the Training/Validation/Test data sets: The training set is used to perform the initial training of the model, initializing the weights of the neural network.

The validation set is used after the neural network has been trained. It is used for tuning the network's hyperparameters, and comparing how changes to them affect the predictive accuracy of the model. Whereas the training set can be thought of as being used to build the neural network's gate weights, the validation set allows fine tuning of the parameters or architecture of the neural network model. It's useful as it allows repeatable comparison of these different parameters/architectures against the same data and networks weights, to observe how parameter/architecture changes affect the predictive power of the network.

Then the test set is used only to test the predictive accuracy of the trained neural network on previously unseen data, after training and parameter/architecture selection with the training and validation data sets.

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