Assessing Models with k-Fold Cross Validation
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's performance on data that's used for training 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 results in a poorer performance on unseen data.
That being said, simply training models on data that's been 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 can introduce bias toward certain models and lead to overfitting.
k-Fold cross validation offers a solution to this problem and allows the variance to be accounted for by way of an error estimate on each accuracy...
