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What is Cross Validation ?
Cross-Validation is a statistical method used to check the performance of a Machine learning model. Cross Validation helps to compare and select appropriate machine learning models for specific predictive problems. It is mainly used to protect against overfitting in a predictive model, In a specific case where the data may be limited. In Cross validation fixed number of fold, run analysis on each fold and then average overall estimate.
Types of Cross Validation
Divide the dataset into two parts: one for training, other for testing
Train the model on the training set
Validate the model on the test set
Repeat 1-3 steps a couple of times. This number depends on the CV method that you are using
There are different types of cross validation methods. Following are some method name that are commonly used for cross validation.
Hold-out
Repeated K-folds
Nested K-folds
K-folds
Time series CV
Leave-one-out
Leave-p-out
Stratified K-folds
Hold-out : The simple approach is to divide our entire dataset into two parts: training data and testing data. We train the model using training data and then evaluate it on the testing set, as the name suggests. The quantity of training data is usually designed to be more than twice that of testing data, resulting in a 70:30 or 80:20 split.
K folds : One technique to improve the holdout method is to use K-fold cross validation. This strategy ensures that our model's score is independent of how we chose the train and test set. The data set is partitioned into k subsets, and the holdout approach is applied to each subset k times.
Stratified K fold : It can be difficult to use K Fold on a classification task. Because we are mixing the data at random and then dividing it into folds, we may end up with severely imbalanced folds, causing our training to be biassed. Let's imagine we get a fold with the majority belonging to one class (say positive) and only a few belonging to the negative class. This will undoubtedly derail our training, therefore we use stratification to create stratified folds.
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