Cross-Validation
It is important to ensure that the machine learning algorithm of choice is effective across several inputs. This means that the machine learning algorithm of choice does not face degradation on the new inputs from the same distribution of the training data.
Cross-validation is a technique for evaluating a machine learning model and testing its performance. CV is commonly used in applied ML tasks. It helps to compare and select an appropriate model for the specific predictive modeling problem.
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