AI-900 Question 117
Single answerYou are building a machine learning model to predict customer churn for a subscription service. As part of the model development process, you split your data into training and validation datasets. What is the primary purpose of using a validation dataset in this scenario?
- A
To train the model on unseen data to improve its performance.
- B
To evaluate the model's performance on data it has not seen during training.
- C
To adjust the weights of the model during the training process.
- D
To ensure the model is capable of generalizing to new, unseen data.
Show answer and explanation
Correct answer: B
Explanation
The validation dataset is used to assess how well the model performs on data it has not seen during training. This helps ensure that the model is not overfitting to the training data and can generalize to new data. In this scenario, the validation dataset plays a critical role in evaluating the model's ability to predict customer churn accurately before it is tested on a final, separate test dataset or deployed in production.
- A. Incorrect.
This is incorrect because the training dataset, not the validation dataset, is used to train the model.
- B. Correct.
This is correct because the validation dataset is specifically used to evaluate the model's performance on unseen data during the model development process.
- C. Incorrect.
This is incorrect because adjusting the weights of the model is part of the training process, which uses the training dataset.
- D. Incorrect.
This is incorrect because while ensuring generalization is a goal of the overall process, the validation dataset's primary purpose is to evaluate the model's performance on unseen data.