Cross-validation and Parameter Tuning

Predictive analytics is about making predictions for unknown events. We use it to produce models that generalize data. For this, we use a technique called cross-validation.

Cross-validation is a validation technique for assessing the result of a statistical analysis that generalizes to an independent dataset that gives a measure of out-of-sample accuracy. It achieves the task by averaging over several random partitions of the data into training and test samples. It is often used for hyperparameter tuning by doing cross-validation for several possible values of a parameter and choosing the parameter value that gives the lowest cross-validation average error.

There are two kinds of cross-validation: exhaustive and non-exhaustive. K-fold is an example of non-exhaustive cross-validation. It is a technique for getting a more accurate assessment...