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
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
Solution
One of the disadvantages of decision trees is that they are prone to overfitting. This means that the model may perform well on the training data but poorly on unseen data. This happens because the decision tree might create too many branches in response to outliers or noise in the training data, leading it to make decisions based on patterns that don't actually exist in the overall data population.
Similar Questions
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.
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
Choose a disadvantage of decision trees among the following.
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
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
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