You build a decision tree to classify five different types of customers based on their browsing history from a sample of 500. The resulting decision tree has 17 layers. One of the leaf nodes has only three customers.
What do you conclude?
A.
The decision tree needs to be rebuilt without the three customers
B.
The decision tree needs to be rebuilt to see if the results change
C.
The sample size is too small, so the classes may not be accurate
D.
Due to large number of layers, there may be an overfitting problem
The decision tree having 17 layers and a leaf node with only three customers suggests that the tree is overfitting the data. This is because a deep tree with many layers can lead to overfitting, where the model fits the training data too closely, capturing noise and small details that do not generalize well to new data. Reducing the tree depth or pruning the tree can help mitigate overfitting.
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