Tag: Theory

In machine learning, complex models often risk overfitting, capturing noise instead of patterns. L1 regularization offers an elegant solution: by penalizing the absolute value of model weights, it not only reduces overfitting but also tends to zero out irrelevant features entirely. This leads to simpler, more interpretable models, especially powerful in high-dimensional spaces. In this…