Understanding Cross-Validation in Machine Learning: Techniques and Best Practices

Cross-validation is a powerful technique used in machine learning to evaluate the performance of a predictive model and to prevent overfitting. It is a method of model validation that divides the original sample into subsets and trains the model on different subsets of the data while validating it on the subset that was not used for training. This process is repeated multiple times with different subsets, and the results are averaged to provide an estimate of the model's performance.

Techniques of Cross-Validation

There are several types of cross-validation techniques, including:

K-Fold Cross-Validation: This is the most common type of cross-validation, where the original dataset is randomly divided into 'k' subsets. The model is then trained on 'k-1' subsets and validated on the remaining subset. This process is repeated 'k' times, with each subset being used for validation once.

Leave-One-Out Cross-Validation (LOOCV): In this method, a single observation is left out for validation, while the model is trained on the remaining 'n-1' observations. This process is repeated 'n' times, with each observation being used for validation once.

Leave-P-Out Cross-Validation: This is similar to LOOCV, but instead of leaving out a single observation, a subset of 'p' observations is left out for validation, while the model is trained on the remaining 'n-p' observations. This process is repeated 'n' times, with different subsets being used for validation each time.

 

Best Practices for Cross-Validation

Choose the Right 'k': The value of 'k' in K-fold cross-validation should be chosen based on the size of the dataset and the computational resources available. A common choice is 'k=10', but this can be adjusted as needed. 

Shuffle the Data: Before performing cross-validation, it is essential to shuffle the data to ensure that each fold contains a representative sample of the entire dataset.

Keep the Same Random Seed: If you are using a random number generator to create folds, make sure to keep the same random seed to ensure the reproducibility of the results.

Use Cross-Validation in Combination with Other Techniques: Cross-validation should be used in combination with other techniques, such as feature selection and regularization, to ensure that the model is robust and performs well on unseen data.

 

References:

"Cross-Validation: Why and How?" by Towards Data Science (https://towardsdatascience.com/cross-validation-why-and-how-c323e47b36c2)