Testing every batch, not just one.
Cross-validation is a method to evaluate how well a machine learning model generalizes to new data by splitting the data into multiple subsets and testing the model on each subset.
Imagine you're baking cookies and want to make sure the recipe works every time. Instead of tasting just one cookie, you taste a few from different batches to be sure they're all good. Cross-validation does the same for AI models: it tests them on different parts of the data to check they're not just memorizing one batch.
Cross-validation prevents overfitting, where a model performs well on training data but fails on new data. You encounter it in any serious machine learning project, from predicting house prices to diagnosing diseases.
Many think cross-validation is about improving the model itself, but it's actually a way to measure performance. It doesn't change the model; it just gives a more reliable estimate of how it will do on unseen data.
Cross-validation is a resampling technique used to assess the predictive performance of a statistical model. It involves partitioning the data into complementary subsets, training the model on one subset (training set), and validating it on another (validation set). K-fold cross-validation repeats this process k times, each time using a different fold as the validation set.