Model evaluation ensures system reliability.
| Card: 2 / 20 |
Fill in the blank: The process of splitting the dataset into training and testing sets is known as ___ split. | Card: 3 / 20 |
Calculate accuracy of ML model.
| Card: 6 / 20 |
True or False: A confusion matrix only provides information about the true positive and true negative values of a model. | Card: 7 / 20 |
False. A confusion matrix also includes false positive and false negative values, providing a complete picture of the model's performance. | Card: 8 / 20 |
F1 Score Represents Precision and Recall
| Card: 10 / 20 |
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Riddle: I help you choose the best prediction model, but I’m not a fortune teller. What am I? | Card: 11 / 20 |
Model evaluation raises ethical concerns.
| Card: 14 / 20 |
Accuracy and error evaluate AI models effectively.
| Card: 16 / 20 |
Fill in the blank: The metric that considers both true positives and false positives to measure the correctness of positive predictions is known as ___ . | Card: 17 / 20 |
How can we interpret a situation where a model has high accuracy but performs poorly in real-world applications? | Card: 19 / 20 |
High accuracy can be misleading.
| Card: 20 / 20 |






