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
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.
Solution
The disadvantage of the decision tree algorithm for classification from the options provided is that it is prone to overfitting with complex trees.
Here's why:
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Decision trees can create complex trees that do not generalize well from the training data to unseen data, a phenomenon known as overfitting. This happens when the tree is designed to perfectly fit all samples in the training data set, so it ends up having a complex structure that is specific to that data and may not work well with new data.
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While decision trees can handle large datasets and missing values, they do not require the data to be linearly separable. Therefore, these are not disadvantages.
So, the correct answer is: "It is prone to overfitting with complex trees."
Similar Questions
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
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
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
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
Choose a disadvantage of decision trees among the following.
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