The correct answer is: C. 2 and 3
Accuracy is not a good idea for imbalanced class problems because it can be misleading. For example, if a model has 99% accuracy on a dataset where the majority class is observed 99% of times, it means that the model is simply always predicting the majority class. This is not a very useful model, because it will not be able to accurately predict the minority class.
Precision and recall are better metrics for imbalanced class problems. Precision measures the fraction of predicted positives that are actually positive, while recall measures the fraction of actual positives that are predicted positive. Both of these metrics are more informative than accuracy in the case of imbalanced class problems.
Here is a more detailed explanation of each option:
- Option 1: Accuracy metric is not a good idea for imbalanced class problems. This is because accuracy can be misleading in the case of imbalanced class problems. For example, if a model has 99% accuracy on a dataset where the majority class is observed 99% of times, it means that the model is simply always predicting the majority class. This is not a very useful model, because it will not be able to accurately predict the minority class.
- Option 2: Accuracy metric is a good idea for imbalanced class problems. This is not correct, as explained above.
- Option 3: Precision and recall metrics are good for imbalanced class problems. This is correct, as precision and recall are more informative than accuracy in the case of imbalanced class problems.
- Option 4: Precision and recall metrics aren’t good for imbalanced class problems. This is not correct, as explained above.