Which preprocessing step should be performed before addressing class imbalance in unsupervised learning?Review LaterOutlier detectionData scalingFeature selectionClustering

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

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.

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

Question 9What approach are you using when you create a new sample of a minority class that does not yet exist?1 pointOversamplingSynthetic OversamplingRandom OversamplingWeighting