How does boosting handle class imbalance?Review LaterBoosting oversamples the majority class to balance the classes.Boosting assigns higher weights to misclassified instances to focus on the minority class.Boosting assigns higher weights to correctly classified instances to focus on the minority class.Boosting uses undersampling to balance the classes.

Which of the following is a disadvantage of boosting algorithms?Review LaterBoosting can lead to underfittingBoosting can be computationally expensiveBoosting requires a large number of hyperparametersBoosting is not suitable for imbalanced data

Which of the following is NOT a common technique for handling imbalanced classes in data preprocessing?a.Undersamplingb.Oversamplingc.Stratified samplingd.Random sampling

Question 1These are all methods of dealing with unbalanced classes EXCEPT:1 pointDownsampling.Mix of in-sample and out-of-sample.Mix of downsampling and upsampling.Upsampling.

Question 8What approach are you using when trying to increase the size of a minority class so that it is similar to the size of the majority class?1 pointRandom OversamplingOversamplingSynthetic OversamplingUndersampling

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.