Cross-validation is a model assessment technique used to evaluate a machine learning algorithm’s performance in making predictions on new datasets that it has not been trained on. This is done by partitioning a dataset and using a subset to train the algorithm and the remaining data for testing. Because cross-validation does not use all of the data to build a model, it is a commonly used method to prevent overfitting during training.
Each round of cross-validation involves randomly partitioning the original dataset into a training set and a testing set. The training set is then used to train a supervised learning algorithm and the testing set is used to evaluate its performance. This process is repeated several times and the average cross-validation error is used as a performance indicator.
Common cross-validation techniques include:
Cross-validation can be a computationally intensive operation since training and validation is done several times. Because each partition set is independent, this analysis can be performed in parallel to speed up the process.
Use MATLAB for Big Data, Machine Learning and Production Analytics Systems.