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
Question
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
Solution
A significant disadvantage of decision trees is that they are prone to overfitting. This means that they can create overly complex trees that do not generalize well from the training data to unseen data. This happens because the decision tree might create too many branches based on the training data and hence, it might not perform well on the new data. This is a common problem in many machine learning models and it's particularly pronounced with decision trees.
Similar Questions
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
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
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
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
Choose a disadvantage of decision trees among the following.
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