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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

Question

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

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Solution

The correct answer is B) A and B. Decision trees require little data preparation and can handle both categorical and numerical data. However, they are not highly sensitive to changes in the training data, meaning a small change in the data does not necessarily result in a large change in the tree.

Similar Questions

Question 3These are two main advantages of decision trees:1 pointThey output both parameters and significance levelsThey are resistant to outliers and output scaled featuresThey do not tend to overfit and are not sensitive to changes in dataThey are very visual and easy to interpret

Choose a disadvantage of decision trees among the following.

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

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 is a disadvantage of decision trees?Review LaterThey are computationally expensive to trainThey are prone to overfittingThey require labeled data for trainingThey are not suitable for high-dimensional data

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