What is one reason for using techniques like K-fold cross-validation?

Using techniques like K-fold cross-validation is essential for maximizing the training dataset used for model evaluation. In K-fold cross-validation, the dataset is partitioned into K distinct subsets (folds), and the model is trained K times, each time using K-1 folds for training and one fold for validation. This method effectively increases the amount of data used for both training and validation because each data point gets to be in the validation set exactly once while being part of the training set in the remaining K-1 iterations.

This approach allows the model to be evaluated multiple times on different subsets of the data, which provides a more robust understanding of the model’s performance and its ability to generalize to unseen data. By utilizing all available data points, K-fold cross-validation helps to mitigate the influence of a potentially biased training-test split that could arise if the evaluation were based solely on a single partition of the dataset. This thorough use of the dataset helps in understanding how the model would perform in real-world scenarios, where data may vary.