Part $2$: Splitting the Data

When training a machine learning model, it is common practice to split the dataset into two separate sets, referred to as

the training set and the test set. The model is created using the data from the training set. After the model is created, its

performance will be evaluated on the test set. The reason for splitting the data in this way is that machine learning models

tend to perform better on the data sets on which they were trained than they do when exposed to new data. If we trained

the model on a data set, and then evaluated it on the same data, we would likely have an overly optimistic view of how well

the model would perform on new observations. By splitting the data, we can hold out a set of observations that are not

seen during training. The held$-$out test data can be used to give as a less biased assessment of how well the model will

perform on new data