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Machine Learning 13/01/2020 1. Which ones among the following answers is correct? a. In regression trees the prediction at each leaf is based on the target variance at the leaf. b. In regression trees the prediction at each leaf is based on the target average at the leaf. c. In regression trees the split at each leaf is based on the target average at the leaf. d. In regression trees the prediction at each leaf is based on the target standard deviation at the leaf. e. In regression trees the split at each node is based on the target variance maximization. f. None of these answers are correct. 2. Referring to the following table, which ones among the following answers is correct? a. By removing “cy1” from Model A, “hp” gets significance b. Both models should be accepted since the adjusted R-square is bigger than 0.5. c. Model B is a better model since it uses more explanatory variables. d. In both models, variable “hp” has a negative correlation with target variable “mpg”. e. Model B should be preferred since it better explains the variance of the target variable. f. None of these answers are correct. 3. Which ones among the following answers is correct? a. Given equal accuracies, multivariate trees usually generate a higher number of classification rules with respect to univariate trees. b. The F-measure is equal to 0 if all the predictions are incorrect. c. Neural networks perform attribute selection automatically. d. A tree is said to be binary if all explanatory attributes are binary. e. The true positive rate is the proportion of correctly classified positive examples. f. None of these answers are correct. 4. Given a dataset X, explain in detail the code below describing the algorithm and each one of the given parameters: from sklearn.neighbors import…