What is cross validation in machine learning?

Question

Can anyone explain cross-validation in machine learning?

Answer 1

Cross-validation in Machine Learning is used to reduce the overfitting of data. In cross-validation, we divide the population into k-subsets. We built a model on one subset and validate on k-1 subsets. So, we will get k models at the end. We calculate the performance metric of all models and consider the average of that metric as an overall performance metric of our model.

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Answer 2

Cross-validation in Machine Learning is a technique that is used to train multiple models on the same input data and later evaluates them based on the same. This is a technique used to decide if the hypothesized relationship between the variables holds, corresponding to the validity of the results obtained. There are many types of cross-validations you can do in Machine Learning. K-Fold cross-validation is a popular method that is widely used. Other methods such as Stratified K-Fold cross-validation and Leave-P-Out cross-validation techniques also see a lot of usages, but each of these techniques has an ability that helps it derive solutions to a variety of problems.

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