Which of the following techniques is used to prevent overfitting in decision tre

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Question: Which of the following techniques is used to prevent overfitting in decision trees?

Options:

Correct Answer: Pruning the tree

Solution:

Pruning the tree involves removing sections of the tree that provide little power in predicting target variables, thus preventing overfitting.

Which of the following techniques is used to prevent overfitting in decision trees?

Which of the following techniques is used to prevent overfitting in decision trees?

Step 1: Understand what overfitting means. Overfitting happens when a model learns too much from the training data, including noise and outliers, making it perform poorly on new data.
Step 2: Learn about decision trees. A decision tree is a model that splits data into branches to make predictions based on certain features.
Step 3: Know that pruning is a technique used to simplify decision trees. It involves cutting off parts of the tree that do not help in making accurate predictions.
Step 4: Realize that by removing these less important branches, the tree becomes simpler and more general, which helps it perform better on unseen data.
Step 5: Conclude that pruning is an effective way to prevent overfitting in decision trees.

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