How do data scientists use cross-validation and holdout samples?

A. Data scientists use cross-validation models and holdout samples to identify a receiver operating characteristing curve that plots the false positive rate on the x-axis and the true positive rate on the y-axis. Comparing these two rates provides insight into how well a model correctly classifies results at any threshold value.

B. Data scientists cross-validate models by comparing predictions of different models on a new set of data for which the actual outcomes are already known. Data scientists also verify that the overall likelihood value of the validation sample used to choose the model is similar to the overall likelihood value of a brand-new and yet-unseen (by the model) holdout sample.

C. Data scientists use cross-validation models and holdout samples to train models. They choose among competing models based on how well the models perform in making classification or predictions on a separate randomly selected data set. Data scientists then use that data set to calculate various statistical measures (R2, t-values, and F-values) that are then used to choose among models.

D. Data scientists use cross-validation models and holdout samples to compare the performance of fully grown vs. pruned decision trees. In doing so, the goal is to minimize the likelihood value of the predictions.

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