Cross-validation is a way to simulate out-of-sample unseen data when developing an algorithm, without actually touching our test set.
Many algorithms include hyperparameters, which are parameters that are characteristic of the algorithm itself rather than the underlying phenomenon. We need to choose the value of these parameters, and we are indifferent to their values beyond their ability to improve predictions. However, computational considerations, such as computation time, could also be a factor in deciding hyperparameter values.
Our algorithm does not account for passengers' ages when making predictions. These are unfortunately, not binary variables, but we can use them to create a binary variable by fixing an age and ...
