1.
What is the primary purpose of a validation dataset in machine learning?

2.
Which metric is commonly used to evaluate classification models and represents the ratio of correctly predicted positive instances to all positive instances?

3.
In k-fold cross-validation, if you choose a higher value of k (e.g., k = 10), what effect does it have on the model evaluation process?

4.
Which technique is used to prevent data leakage in cross-validation, ensuring that information from the test set doesn't influence model training?

5.
What is the purpose of the Receiver Operating Characteristic (ROC) curve in model evaluation?

7.
What does the term "overfitting" refer to in the context of model evaluation and validation?

8.
In model evaluation, what is the primary goal of feature scaling or normalization?

9.
Which method involves splitting the dataset into three parts: training, validation, and test sets, ensuring that the model's performance is assessed on unseen data?

10.
What is the primary advantage of using stratified sampling in model evaluation?