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Lasso
L1Regularization
Overfitting
L2Regularization

What is L1 regularization, and how does it differ from other regularization techniques like L2 regularization? Explain the role of L1 regularization in the context of feature selection and model complexity. Discuss how L1 regularization affects the model's coefficients and its ability to perform automatic feature selection by encouraging sparsity. Additionally, highlight scenarios or types of datasets where L1 regularization might be more beneficial or less effective compared to other regularization methods.

machine learning
Intermediate Level

Regularization techniques like L1 and L2 are used to prevent overfitting in machine learning models by penalizing large coefficients.

L1 regularization, also known as Lasso regularization, adds a **penalty term proportional to the absolute value of the coefficients...

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