Which of the following is NOT the same across Classification and Regression Decision Trees?A. Type of predicted variableB. Performance MetricAnswer choicesSelect only one optionREVISITOnly AOnly BBoth A and B

Which of the following is/are the advantages(s) of decision tree?A) It requires little data preparationB) It can handle both categorical and numerical dataC) A small change in the training data will result in a large change in the treeAnswer choicesSelect only one optionREVISITOnly AA and BB and CA and C

Which of these is a potential limitation of using Decision Trees?Answer choicesSelect only one optionREVISITThey are computationally very expensive and slow to trainThey can easily overfit and won't always produce the best results when asked to generalizeThey are very unsuitable for regression tasksThey can only handle small datasets

Which of the following are the advantage/s of Decision Trees? Tick all the correct options.3 pointsPossible Scenarios can be addedUse a white box model, If given result is provided by a modelWorst, best and expected values can be determined for different scenariosAll of the above

Question 2A Covid dataset is generated by measurements of a few health parameters for various individuals. A Decision tree Classifier is applied to this dataset to see if we can predict whether an individual is Covid-Positive (1) or Covid-Negative(0). Which of the following is the most important performance metric in this case?Answer choicesSelect only one optionREVISITPrecisionRecallCannot be determinedNone of the above

Question 1These are all characteristics of decision trees, EXCEPT:1 pointThey have well rounded decision boundariesThey split nodes into leavesThey can be used for either classification or regressionThey segment data based on features to predict results