What is a disadvantage of using Decision Trees in real-world applications?

What is a disadvantage of using Decision Trees in real-world applications?

Step 1: Understand what a Decision Tree is. It is a model that makes decisions based on asking a series of questions about the data.
Step 2: Learn about overfitting. This happens when a model learns the training data too well, including its noise and outliers.
Step 3: Realize that if a Decision Tree is too complex, it can memorize the training data instead of generalizing from it.
Step 4: Recognize that overfitting leads to poor performance on new, unseen data because the model is too tailored to the training data.
Step 5: Conclude that one disadvantage of Decision Trees is their tendency to overfit, especially with complex datasets.

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