How does NumPy's linear regression compare to scikit-learn's?

How does NumPy's linear regression compare to scikit-learn's?

NumPy provides a basic implementation of linear regression, focusing on matrix operations, while scikit-learn offers a more comprehensive framework with additional features like cross-validation and hyperparameter tuning.

What are the advantages of using scikit-learn over NumPy for linear regression?

Scikit-learn simplifies the process with built-in functions for model evaluation, feature selection, and easier integration with pipelines, making it more user-friendly for complex tasks.

Can I use NumPy for large datasets?

Yes, NumPy can handle large datasets, but it may require more manual work for optimization and performance compared to scikit-learn, which is optimized for such tasks.

Which one should I choose for a beginner?

For beginners, scikit-learn is recommended due to its simplicity and extensive documentation, making it easier to learn and implement machine learning models.