10 6. a Write differences between bootstrapping and cross-validation. b+ Briefly explain the steps of Stacking as an ensemble classifier. Suppose in a classification problem, you have the probabilities of the three models: M1, M2, M3 (shown in Table 2) for five observations of test data set. Table 2: Output of three machine learning models Output M1 1.70 .50 .30 49 .60 M2 .80 .64 1.20 M3 .75 .80 1.35 .50 .51 .80 .60 What will be the predicted category for these observations if you apply probability threshold greater than or equals to 0.5 for category 1 or less than 0.5 for category "O"? Fill the table with the category you have determined. Briefly explain the working principle of random forest algorithm. d)