Cross Validation
Cross validation is a statistical technique used in machine learning and data analysis to assess how well a predictive model will generalize to an independent dataset. It involves partitioning a dataset into complementary subsets, training the model on one subset (training set), and validating it on the other subset (validation set) to evaluate performance. This process is typically repeated multiple times with different partitions to reduce variability and provide a more robust estimate of model accuracy.
Developers should learn cross validation when building machine learning models to prevent overfitting and ensure reliable performance on unseen data, such as in applications like fraud detection, recommendation systems, or medical diagnosis. It is essential for model selection, hyperparameter tuning, and comparing different algorithms, as it provides a more accurate assessment than a single train-test split, especially with limited data.