Choose a disadvantage of decision trees among the following.Decision trees are robust to outliersFactor analysisDecision trees are prone to overfitAll of these

Disadvantage of Decision Trees

Among the options provided, the correct choice is:

Decision trees are prone to overfit.

Explanation

Decision trees create models that can become overly complex, capturing noise in the data rather than the underlying patterns. This leads to a situation where the model performs well on training data but poorly on unseen data (test data), which is known as overfitting. While decision trees have robust characteristics, they are particularly vulnerable to this issue, especially when they are not properly pruned or configured with parameters to limit their depth or complexity.

The other options listed, such as them being robust to outliers and factor analysis, do not represent disadvantages. Thus, when evaluating the robustness of decision trees, their tendency to overfit is a significant concern.