Machine Learning for OpenCV 4 - Second Edition

Combining grid search with cross-validation

One potential danger of the grid search we just implemented is that the outcome might be relatively sensitive to how exactly we split the data. After all, we might have accidentally chosen a split that put most of the easy-to-classify data points in the test set, resulting in an overly optimistic score. Although we would be happy at first, as soon as we tried the model on some new held-out data, we would find that the actual performance of the classifier is much lower than expected.

Instead, we can combine grid search with cross-validation. This way, the data is split multiple times into training and validation sets, and cross-validation is performed at every step of the grid search to evaluate ...