Cross-validation is a kind of model validation technique used in machine learning. It is basically used the subset of the data-set and then assess the model predictions using the complementary subset of the data-set. K-fold cross-validation is one of the popular methods used under this technique to evaluate the model on the subset that was not used for training the model.
Under the K-fold cross-validation, the entire data is divided into k subsets and the holdout method is repeated k times such that each time one of the k subsets is used as the test set/validation set and the other k-1 subsets are put together to form a training set.
This popular method because it is simple to understand all machine learning engineers because it generally results in a less biased or less optimistic estimate of the model skill than other methods, such as a simple train/test split.
Process of K-fold Cross-Validation
First Process: Shuffle the dataset randomly.
Second Process: Split the dataset into k groups.
For each unique group:
Step 1: Take the group as a holdout or test data set
Step 2: Take the remaining groups as a training data set
Step 3: Fit a model on the training set and evaluate it on the test set
Step 4: Retain the evaluation score and discard the model
Third Process: Summarize the skill of the model using the sample of model evaluation scores.
It is very much important that each observation in the data sample is assigned to an individual group and remains in that group for the duration of the procedure. That means each sample is provided the chance to used in the hold out set 1 time and used to train the model k-1 times. As the results of a k-fold cross-validation run are often summarized with the mean of the model skill scores.
Leave One Out Cross-Validation
Similarly, LOOCV (Leave One Out Cross Validation) is another cross-validation method the validation process is performed by training on the whole data-set except only one data-point of the available data-set and then iterates for each data point. The benefit of using this method is that it leads to higher variation in the testing model as we are testing against one data point.
While the disadvantage of using this method is it takes a lot of execution time as it iterates over “the number of data points” times. This method is generally chosen over the previous one because it does not suffer from the intensive computation, as the number of possible combinations is equal to the number of data points in the original sample or n.
Why Cross-Validation in Machine Learning is Used?
The cross-validation machine learning technique is very useful for evaluating the effectiveness of your model mainly when you need to mitigate over-fitting. However, it is also used in determining the hyperparameters of your model, in terms of finding which parameters will give results in the lowest test error. It is one of the most widely used and effective techniques of machine learning model validation used by machine learning engineers worldwide to create a fully functional AI model with the best level of accuracy for flawless results.
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