Which of the following techniques can help prevent overfitting?

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Question: Which of the following techniques can help prevent overfitting?

Options:

Correct Answer: Cross-validation

Solution:

Cross-validation is a technique that helps prevent overfitting by ensuring the model\'s performance is evaluated on different subsets of the data.

Which of the following techniques can help prevent overfitting?

Which of the following techniques can help prevent overfitting?

Step 1: Understand what overfitting means. Overfitting happens when a model learns the training data too well, including its noise and outliers, making it perform poorly on new data.
Step 2: Learn about cross-validation. Cross-validation is a method used to assess how well a model will perform on unseen data.
Step 3: Realize how cross-validation works. It involves splitting the data into several parts (or folds). The model is trained on some parts and tested on others.
Step 4: Recognize the benefit of cross-validation. By evaluating the model on different subsets of data, it helps ensure that the model generalizes well and does not just memorize the training data.

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