Describe how training and validation datasets are used in machine learning

In ML, training and validation sets are subsets of your overall dataset used during the model development phase. Their roles are distinct but complementary, aimed at creating a model that is able to make accurate predictions about new, unseen data.

You may recall seeing the concepts of training and validation sets in Chapter 3, Identify Common Machine Learning Techniques when we discussed dividing the dataset into sections—a subset that would be used to train the model, and a “held back” or “reserved” part of the data that we could use to test the predictions. These are the training and validation sets, respectively.

Training set

This is the data on which the ML model is trained. The model learns to make predictions or decisions based on this data. The training set is used to fit the parameters of the model, such as the weights in a neural network (NN) or the coefficients...