Model fitting and evaluation

In this part of the machine learning process, we have the model and data ready, and we proceed to train and validate our model.

Dataset partitioning

At the time of training the models, we usually partition all the provided data into three sets: the training set, which will actually be used to adjust the parameters of the models; the validation set, which will be used to compare alternative models applied to that data (it can be ignored if we have just one model and architecture in mind); and the test set, which will be used to measure the accuracy of the chosen model. The proportions of these partitions are normally 70/20/10.

Common training terms –  iteration, batch, and epoch

When training the model, there are some common terms that indicate the different parts of the iterative optimization:

• An iteration defines one instance of calculating the error gradient and adjusting the model parameters. When the data is fed into groups of samples, each one of these groups...