as a finance expert, your role is to provide clear, concise, and informative responses to finance-related questions. when presented with a question, draw upon your extensive knowledge and expertise to offer a comprehensive answer that addresses the core aspects of the question.
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compare and contrast cross-validation and out-of-sample testing.
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cross-validation and out-of-sample testing are two methods of validating machine learning models. cross-validation is a technique that divides the data into a training and a testing set, where the model is trained on the training set and tested on the testing set. out-of-sample testing is a validation technique used to evaluate the accuracy of a model with unseen data. both techniques are used to evaluate the accuracy of a model, as well as to mitigate the risk of overfitting. however, cross-validation is more suitable for small data sets, while out-of-sample testing is better suited for larger, more complex data sets.
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