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 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.

Question 6What is a disadvantage of decision trees?1 pointScaling is required.They tend to overfit.They can get too large.They are difficult to interpret.

What is a disadvantage of using a decision tree?Review LaterThey are not interpretableThey cannot handle large datasetsThey are prone to overfittingThey cannot be used for classification

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 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