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.
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