Describe the process of performing cross-validation as part of model fitting in a machine learning pipeline. Include details on how to implement this in a code snippet using Python and the scikit-learn library, and explain the benefits of integrating cross-validation into the pipeline.

Simulated

Cross-validation is performed by training the model multiple times on the entire dataset to ensure maximum learning.

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Cross-validation involves splitting the data into training and validation sets once, then training the model on the training set and validating on the validation set.

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Cross-validation in a pipeline ensures that the model is trained and validated on different subsets of the data, providing a more robust estimate of model performance and helping to prevent overfitting.

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Cross-validation is only applicable to classification problems and not to regression problems.

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Describe the process of performing cross-validation as part of model fitting in a machine learning pipeline. Include details on how to implement this in a code snippet using Python and the scikit-learn library, and explain the benefits of integrating cross-validation into the pipeline.