Python Machine Learning: Machine Learning and Deep Learning with Python, scikit-learn, and TensorFlow

by Sebastian Raschka

A classic and popular approach for estimating the generalization performance of machine learning models is holdout cross-validation. Using the holdout method, we split our initial dataset into a separate training and test dataset—the former is used for model training, and the latter is used to estimate its generalization performance. However, in typical machine learning applications, we are also interested in tuning and comparing different parameter settings to further improve the performance for making predictions on unseen data. This process is called model selection, where the term model selection refers to a given classification problem for which we want to select the optimal values of tuning parameters (also called hyperparameters). However, if we reuse the same test dataset over and over again during model selection, it will become part of our training data and thus the model will be more likely to overfit. Despite this issue, many people still use the test set for model selection, which is not a good machine learning practice. A better way of using the holdout method for model selection is to separate the data into three parts: a training set, a validation set, and a test set.