Machine Learning Test Datasets Answers 1. What is the primary purpose of a test dataset in machine learning? A. To validate the training dataset B. To evaluate the model's performance on unseen data C. To train the machine learning model D. To increase the model's complexity Answer: To evaluate the model's performance on unseen data (B) A test dataset is specifically used to evaluate how well a model performs on data it has not been trained on. 2. Why is it important to have a separate test set? A. To ensure the model has more training data B. To allow for an unbiased evaluation of the model's performance C. To simplify the evaluation metrics used D. To prevent the model from underfitting Answer: To allow for an unbiased evaluation of the model's performance (B) A separate test set is crucial for providing an unbiased assessment of the model's true predictive power. 3. Which of the following is NOT a characteristic of a good test dataset? A. It can include data points from the training dataset B. It is very large in size C. It is mutually exclusive of the training dataset D. It represents the real-world data distribution Answer: It can include data points from the training dataset (A) A good test dataset must be mutually exclusive of the training dataset to maintain its validity for evaluation. 4. What is one method for ensuring that a test dataset remains representative in case of imbalanced data? A. Randomly selecting data from the entire dataset B. Using a larger training dataset C. Creating a smaller test set D. Employing stratified sampling Answer: Employing stratified sampling (D) Stratified sampling ensures proportionate representation of classes, making the test dataset more reflective of real-world scenarios. 5. What common mistake could invalidate the purpose of a test dataset? A. Using it exclusively for evaluation B. Making sure it represents the real-world distribution C. Collecting data in an unbiased manner D. Employing it in model training phases Answer: Employing it in model training phases (D) Using the test dataset for training or validation compromises its role in objectively evaluating the model's performance. 6. Which evaluation criterion is NOT typically relevant for assessing a machine learning model's performance? A. Accuracy B. F1-score C. Precision D. Number of features in the model Answer: Number of features in the model (D) The number of features is not an evaluation criterion; instead, metrics like accuracy and precision are critical for performance assessment. 7. What should a researcher do if they encounter data quality issues in the test dataset? A. Reduce the size of the test dataset B. Use only predictions rather than actual data points C. Handle missing values, outliers, and inconsistencies D. Ignore the data quality issues as they will not affect results Answer: Handle missing values, outliers, and inconsistencies (C) Addressing data quality issues is essential to ensure the accuracy and reliability of model evaluation. 8. What is a common practice for splitting datasets in machine learning? A. Creating a test set only after model validation B. Randomly splitting into training, validation, and test sets C. Manual selection of cases for the test set D. Using full dataset for both training and testing Answer: Randomly splitting into training, validation, and test sets (B) Randomly splitting the dataset into training, validation, and test sets is a widely used method in machine learning to ensure proper evaluation.