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.

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

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

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 7When working with unbalanced sets, what should be done to the samples so the class balance remains consistent in both the train and test set?1 pointStratify the samplesUse oversamplingUse a combination of oversampling and undersamplingApply weighted observations