1 point Which of the followings can be said about random forest ensemble model? Random forest method averages out the noise in the data. Random forest method is simpler than a decision tree as it does not require bagging. Random forest method increases variance across samples. 1point In decision tree method, when we decide on the optimal split, we want the child node (complete the sentence). to contain the response variable, y, as heterogenous (diverse) as possible. to contain the response variable, y, as homogeneous (alike) as possible. to show higher correlation between observations. to be rebellious against the parent node. 1 point Q We are using decision tree method to estimate a continuous target variable (y) in a regression setting. What is the prediction rule to estimate the target variable at each leaf? The prediction of crystal ball that was used at each leaf to predict y. The percentage of the sample y values that end up in that leaf Conditional probability of the sample y values that end up in that leaf The average value of the sample y values that end up in that leaf Joint probability of the sample y values that end up in that leaf