Which is NOT a typical step in preparing data for training a decision tree?Review LaterOne-hot encoding categorical variablesNormalizing featuresHandling missing valuesConverting target variable into categorical format

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 is a significant disadvantage of decision trees?Answer areaThey are difficult to interpretThey require a lot of dat preprocessingThey are prone to overfittingThey are not suitable for categorical data

Which of the following is a method for handling categorical variables in a supervised learning model?Review LaterOne-hot encodingLabel encodingbinary encodingall the above

True or false? Decision trees are only suitable for handling categorical data.A:    FalseB:    True

Which of the following is a disadvantage of the decision tree algorithm for classification?It is not suitable for handling large datasets.It cannot handle missing values in the dataset.It requires the data to be linearly separable.It is prone to overfitting with complex trees.