What intuitive technique is used for unbalanced datasets that ensures a continuous downsample for each of the bootstrap samples?1 pointDownsamplingUpsamplingSMOTEBlagging

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 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

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

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

1.Question 1Which of the following statements about Downsampling is TRUE?1 pointDownsampling is likely to decrease Precision.Downsampling results in excessive focus on the more frequently-occurring class.Downsampling is likely to decrease Recall.Downsampling preserves all the original observations.