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 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

When evaluating all possible splits of a decision tree what can be used to find the best split regardless of what happened in prior or future steps?1 pointGreedy SearchRegularizationClassificationLogistic regression

Topic: Classification & Regression When selecting a decision tree split criterion, which is a reason to choose Gain Ratio over Information Gain? (Select ANY correct answer) A. you have polynominal attributes with many values. B. you need to get the fastest runtime (Gain Ratio always has a shorter runtime than Information Gain). C. you have a relatively small data set (they will both take similar time to run but Gain Ratio always gives better performance over Information Gain). D. you want a criterion that takes Information Gain, and adjusts it for each attribute based on the number of possible values.

_______ is a technique that reduces the size of decision trees by removing branches of the trees to avoid overfitting in a fully grown decision tree.Answer choicesSelect only one optionREVISITCross-validationPruningTest-Train SplittingBootstrapping

How is the best split determined at each node while building a Decision Tree?Answer choicesSelect only one optionREVISITWe split the data using the first independent variable and so on. based on the alphabetical orderThe first split is determined randomly and from then on we start choosing the best split.We make at most 5 splits on the data using only one independent variable and choose the split that gives the highest Information Gain.