Consider the validation set approach. We estimate the model on the training set, and make predictions on the validation set.

Question

BlackTom AI · Accepted Answer

The statement describes a common workflow in the validation set approach.

Key idea: you train or estimate the model using the training set data, then you generate predictions on a separate validation set. This separation aims to provide an unbiased estimate of how the model will perform on unseen data, since the model has not been trained on the validation instances.

Why this is coherent: the training set is used to fit the model parameters, while the validation set serves as a proxy for generalization performance. Predictions on the validation set are then compared to the true labels to assess metrics such as accuracy, RMSE, or others, depending on the task.

Potential nuances: the term 'validation set approach' can sometimes be used interchangeably with simply having a hold-out set for evaluation, but the core concept remains: train on one subset and evaluate on another.

What could make this statement erroneous in other contexts: if one were to train on the validation set (i.e., leak data from validation into training) or to evaluate on the training set itself, that would defeat the purpose of validation by giving an overly optimistic performance estimate.

In this specific description, the described separation and procedure aligns with the standard validation paradigm: train on the training set and predict (and evaluate) on the validation set.

Note: since no answer options were provided, this reasoning assesses the given statement in isolation and clarifies how the validation set approach is typically construed.

QBUS6810 (ND) Week 6 Quiz

Consider the validation set approach. We estimate the model on the training set, and make predictions on the validation set.

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