When training complex machine learning models, regularization techniques are important to prevent overfitting. Two common types of regularization are L1 and L2. In this post, we'll compare L1 and L2 Regularization and see how they can be applied.
Why Use Regularization?
Many machine learning models have a large number of parameters that enable them to fit very complex patterns. This flexibility can lead to overfitting - when a model fits the noise in the training data too closely and fails to generalize. Regularization constrains models to avoid overfitting.
Comparing L1 and L2 Regularization
Here is a summary of the key differences between L1 and L2 Regularization:
|Zeroes out weights
|Shrinks weights towards 0
|Smaller weights, less extreme values
L1 Regularization helps drive non-important feature weights to zero, removing them from the model. This leads to sparse models that perform feature selection.
L2 Regularization shrinks the weights towards zero but does not completely remove features. This helps handle high correlations between features by reducing extreme weights.
Applying Regularization in Scikit-Learn
Here is some sample Python code using Scikit-Learn to apply L1 and L2 Regularization:
from sklearn.linear_model import Lasso, Ridge
lasso = Lasso(alpha=1.0)
print(lasso.coef_) # Sparse model
ridge = Ridge(alpha=1.0)
print(ridge.coef_) # No zeros
The Lasso model uses L1 Regularization, while the Ridge model uses L2 Regularization. We can see the coefficient vectors reflect their regularization effects.
L1 Regularization is useful when feature selection is needed, while L2 handles correlations between features. Properly tuning regularization strength is critical to prevent overfitting without losing model flexibility. Regularization provides an important method for improving generalization.