From the course: Deep Learning with Python: Optimizing Deep Learning Models

Lasso and ridge regularization - Python Tutorial

From the course: Deep Learning with Python: Optimizing Deep Learning Models

Lasso and ridge regularization

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- [Instructor] Regularization is a crucial technique employed to prevent overfitting. A scenario where a model learns the training data too well, including the noise and minor fluctuations that do not represent the true patterns. Overfitting leads to a model that performs well on training data, but struggles to generalize effectively to unseen data. To address this, L1 and L2 regularization are two widely used methods that add a penalty to the loss function during training, thereby encouraging simpler models and reduce the likelihood of overfitting. L1 regularization, also known as lasso regularization, modifies the loss function by adding the sum of the absolute values of the weights as a penalty term. Mathematically, L1 regularization is expressed as shown here, where L represents original loss function, lambda is a regularization parameter that controls the strength of the penalty, and wi are the weights or parameters of the model. By adding the absolute values of the weights, L1 regularization encourages sparsity, meaning that it drives some weights to exactly 0. This effectively removes those features from the model, leading to simpler, more interpretable models, where only the most significant features contribute to the final prediction. This characteristic makes L1 regularization particularly valuable for feature selection, especially when dealing with high dimensional data where many features may be irrelevant. For instance, consider a model trained on a dataset with thousands of features where only a subset is actually meaningful for the task at hand. Applying L1 regularization helps in automatically selecting these relevant features by forcing the less important ones to have a 0 weight, simplifying the model and enhancing its interpretability. However, while the model becomes simpler and potentially less prone to overfitting, it may also exclude features that could have contributed minor yet useful information. L2 regularization, also known as ridge regularization, modifies the loss function by adding the sum of the squared values of the weights as a penalty term. Mathematically, L2 regularization is expressed as shown here. Unlike L1, L2 regularization does not push weights to exactly 0. Instead, it discourages large weight values by penalizing the squared magnitudes, resulting in smaller and more evenly distributed weights across the network. This type of penalty reduces the model's reliance on any single feature, promoting generalization by making the model more robust to variations in the data. L2 regularization is particularly effective in situations where all input features are expected to contribute meaningfully to the prediction what should be controlled to prevent overfitting. For example, in a deep learning model used for image classification where every pixel might hold some importance, L2 regularization helps balance the contribution of each feature by preventing some weights from becoming excessively large. This helps maintain a smooth decision boundary, which is crucial for making accurate predictions on new data. Choosing between L1 and L2 regularization depends on the specific requirements of the problem at hand. In summary, use L1 regularization when you expect that only a subset of features are relevant and you need feature selection as part of the training process. Use L2 regularization when you want to control the weights and prevent overfitting without removing any feature from consideration.

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