Thus far, we have trained models on a subset of the data and then assessed performance on the unseen portion, called the test set. This is good practice because the model performance on training data is not a good indicator of its effectiveness as a predictor. It's very easy to increase accuracy on a training dataset by overfitting a model, which can result in poorer performance on unseen data.
That said, simply training models on data split in this way is not good enough. There is a natural variance in data that causes accuracies to be different (if even slightly) depending on the training and test splits. Furthermore, using only one training/test split to compare models ...
