You are solving a classification problem with a highly imbalanced class. The majority class is occurring 98% of times in the training data.Now when you come with predictions, your model has 98% accuracy on test data. Which of the following is true in such a case?1. Accuracy metric is not a good idea for imbalanced class problems.2. Accuracy metric is a good idea for imbalanced class problems.3. Precision and recall metrics are good for imbalanced class problems.4. Precision and recall metrics aren’t good for imbalanced class problems.

In a classification problem, what metric would you use to measure the performance of a model when the classes are imbalanced?*1 pointo A) Accuracyo B) Precisiono C) Recallo D) F1-score

Which of the following statements is true regarding class imbalance?Group of answer choicesA label with class imbalance will always be challenging to model.An accuracy of 99% can be bad when there is high class imbalance.Upsampling is always better than downsampling.Class imbalance is a pretty rare problem to worry about.

2. You need to evaluate a classification model. Which metric can you use? ​​Mean squared error (MSE)PrecisionSilhouette

Which evaluation metric is commonly used for binary classification problems and measures the proportion of true positive predictions among all positive examples?Select one:a. Recallb. Precision

You are evaluating a binary classifier. There are 50 positive outcomes in the test data, and 100 observations. Using a 50% threshold, the classifier predicts 40 positive outcomes, of which 10 are incorrect.The threshold is now increased further, to 70%. Which of the following statements is TRUE?1 pointThe Recall of the classifier would Increase.The Precision of the classifier would decrease.The Recall of the classifier would increase or remain the same.The Precision of the classifier would increase or remain the same.