Which of the following statements is not true about the Decision tree?1 pointa) It starts with a tree with a single leaf and assign this leaf a label according to a majority vote among all labels over the training setb) It performs a series of iterations and on each iteration, it examine the effect of splitting a single leafc) It defines some gain measure that quantifies the improvement due to the splitd) Among all possible splits, it either choose the one that minimizes the gain and perform it, or choose not to split the leaf at all

The following image shows the performance of a decision tree model using split validation. Which of following best describes what you learn from this information? (Select ONE correct answer)A.The model is wrong more than 1/5 of the time. So it cannot be used.B.The model is wright more than 3/4 of the time. So it is ready for production.C.The model accuracy, recall, and precision are all above 70%. So it is ready for production.D.This information isn't sufficient. It must be compared with a baseline, like the existing decision making process.

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

What are the disadvantages of the decision tree?*1 point(A) Over-fitting of the data is possible.(C) We have to balance the dataset before training the model(B) The small variation in the input data can result in a different decision tree(D) All 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

Question 2When splitting data into branches for a decision tree, what kind of feature is favored and chosen first?1 pointThe feature that increases purity in the tree nodes.The feature with the greatest number of categories.The feature that splits the data equally into groups.The feature that increases entropy in the tree nodes.