K-Fold Cross Validation is a technique for assessing how a machine learning model generalizes to new data. It involves dividing the data into ‘k’ subsets and repeatedly training and testing the model with different subsets acting as the test data.


Imagine you want to practice for a quiz and you have 10 sets of question cards. Instead of using all 10 sets to practice and leaving none for testing your knowledge, you can use K-Fold Cross Validation. You use 9 sets for practice, and the 10th set for testing. Now, to make sure you’re not being biased by the questions in the 10th set, you repeat this process 10 times, each time choosing a different set of questions as your test set. This is the idea of K-Fold Cross Validation, where ‘k’ is the number of question sets, or in AI terms, subsets of your model’s training data.

In-depth explanation

In machine learning, K-Fold Cross Validation is a statistical method used to estimate the performance of a model on unseen data, meaning future data it may be asked to predict. The objective is to test the model’s capability to predict new data that was not used in model training, so as to avoid overfitting and underfitting.

The process of K-Fold Cross Validation involves the division of the original sample into ‘k’ equal sized subsamples or folds. Out of these ‘k’ folds, a single fold is kept as the validation data for testing the model, and the remaining ‘k-1’ folds are used as training data. The cross-validation process is then repeated ‘k’ times (the folds), with each of the ‘k’ subsamples used exactly once as the validation data.

The ‘k’ results from the folds can then be averaged (or otherwise combined) to produce a single estimation. In this way, K-fold Cross Validation is able to provide a thorough exploration of how the model responds to a variety of training and validation datasets, thus providing insights into whether the model has accurately learnt the patterns in the data or whether it has perhaps overfitted or underfitted

One of the main benefits of K-Fold Cross Validation is that all observations are used for both training and validation, and each observation is used for validation exactly once. It can help data scientists ensure that their model is robust and reliable, prior to deploying the model in a production setting.

It’s important to note that the value of ‘k’ chosen depends on the size of the dataset and what you think is the best strategy. A typical value used in practice is k=10. K-Fold Cross Validation can lead to a model that generalizes well, if the right value of ‘k’ is chosen.

Train-Test Split, Overfitting, Underfitting, Validation Set, Generalization, Model Selection, Bias-Variance Tradeoff, Machine Learning (ML), Model Evaluation