Explore how validation sets play a crucial role in evaluating model performance and refining predictions in machine learning. Understand their significance for accurate model training and predictions.
Table of Contents
Question
For each of the following statements, select Yes if the statement is true. Otherwise, select No.
Statement 1: A validation set includes the set of input examples that will be used to train a model.
Statement 2: A validation set can be used to determine how well a model predicts labels.
Statement 3: A validation set can be used to verify that all the training data was used to train the model.
Answer
Statement 1: A validation set includes the set of input examples that will be used to train a model: No
Statement 2: A validation set can be used to determine how well a model predicts labels: Yes
Statement 3: A validation set can be used to verify that all the training data was used to train the model: No
Explanation
Statement 1: A validation set includes the set of input examples that will be used to train a model: No
The validation dataset is different from the test dataset that is held back from the training of the model.
Statement 2: A validation set can be used to determine how well a model predicts labels: Yes
A validation dataset is a sample of data that is used to give an estimate of model skill while tuning model’s hyperparameters.
Statement 3: A validation set can be used to verify that all the training data was used to train the model: No
The Test Dataset, not the validation set, used for this. The Test Dataset is a sample of data used to provide an unbiased evaluation of a final model fit on the training dataset.
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