What is Cross-validation?
Cross-validation is a technique for assessing how the results of a statistical analysis will generalize to an independent data set. It is mainly used in settings where the goal is prediction, and one wants to estimate how accurately a predictive model will perform in practice.
What is the advantage of Cross-Validation?
The advantage of cross-validation is that it allows you to assess the performance of your model on a dataset without having to hold out a separate validation set. This means that you can use all of your data to train your model, and still get an estimate of its performance.
What are different types of Cross-Validation techniques?
Following are the various Cross-Validation techniques detailed.
- Holdout method: This method involves randomly dividing the dataset into a training set and a test set. The model is then fit on the training set and evaluated on the test set. This approach can be used when there is a large amount of data available.
- K-fold cross-validation: This method involves randomly splitting the dataset into K folds (typically K = 10). The model is then fit on K-1 folds and evaluated on the remaining fold. This process is repeated K times, with each fold serving as the test set once. The final model is then evaluated on the entire dataset. This approach can be used when there is a limited amount of data available.
- Leave-one-out cross-validation: This method a.k.a LOO-CV involves leaving out one data point from the dataset and fitting the model on the remaining data points. The model is then evaluated on the data point that was left out. This process is repeated for all data points in the dataset. This approach can be used when there is a limited amount of data available and when each data point is important.
- Stratified cross-validation: This method is similar to k-fold cross-validation, but it ensures that each fold contains an equal proportion of data from each class (if the dataset is labeled). This approach can be used when there are a small number of data points available.
Which is the most commonly used Cross-Validation technique?
The most commonly used Cross-Validation technique is the k-fold Cross-Validation.