When do we use regression trees instead of decision trees?1 pointWhen all of the independent variables are continuousWhen some of the independent variables are continuousWhen the response is continuous instead of categoricalWhen the response is categorical instead of continuous

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

Decision trees where the target variable can take continuous values (typically real numbers) are calledSelect one:a. Regression treesb. Classification trees

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

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

Question 8What is an advantage of using Decision Trees over other machine learning methods?1 pointThey are simple to understand and interpret and can work on small datasets. They can be used for both classification and regression, whereas others are limited to one or the other.They can overfit datasets better than other methods because of the number of parameters learned.They allow for the processing of unstructured data, such as images and sequences of text.