Think Different

                                                         Photo by Aqib Shahid L1 and L2 Regularization Explained: Both L1 and L2 regularization are techniques used in machine learning to prevent overfitting and improve the generalization ability of models. They achieve this by penalizing large parameter values, but do so in different ways: L1 Regularization (Lasso Regression): Adds the absolute value of the magnitude of each coefficient (weight) as a penalty term to the loss function. Shrinks some coefficient values to zero , effectively sparsifying the model and selecting relevant features. More robust to outliers compared to L2, as absolute values are less sensitive to extreme values. Interpretability is improved as zero coefficients can indicate features irrelevant to the model. L2 Regularization (Ridg...