Regularization in machine learning is used to prevent overfitting in models, particularly in cases where the model is complex and has a large number of parameters.

Overfitting occurs when a model becomes too closely aligned with its training data, resulting in poor performance on unseen data. Regularization techniques can reduce overfitting by adding the constraint/penalty to the loss function.

In this blog, we will learn about 5 most popular regularization techniques used in machine learning, particularly in deep neural networks with multiple layers of neurons.

## 1. Lasso Regression/L1

Lasso regression, also known as L1 regularization, adds a constraint/penalty term to the loss function that is proportional to the absolute value of the model parameters.

Here, 𝐿(𝛽) is the loss function, 𝑛 is the number of samples, 𝑝 is the number of features, λ is the regularization parameter, and 𝛽𝑗 are the model parameters.

L1 helps prevent overfitting by shrinking the less important weights to zero, effectively removing some features from the model. It is particularly useful for feature selection, as it can help identify the most important features in the dataset.

## 2. Ridge Regression/L2

Ridge regression, also known as L2 regularization, adds a penalty term to the loss function that is proportional to the square of the model parameters, keeping the model weights as small as possible. Unlike L1 regularization, it does not force any weights to be exactly zero.

Increasing the regularization parameter 𝜆 results in an underfit model, while decreasing 𝜆 leads to overfitting. We just have to find the balance.

L2 regularization is also useful when dealing with multicollinearity in the input features, as it helps to stabilize the parameter estimates.

## 3. Elastic Net Regularization

Elastic Net Regularization is a combination of L1 and L2 penalties. It is a more stable regularization function as we get balanced feature selection and parameter shrinkage. It is particularly useful when dealing with high-dimensional data (hundreds of features) with many correlated features.

Here, λ 1 and 𝜆 2 are regularization parameters that control the balance between L1 and L2 penalties.

Elastic Net regularization can help reduce overfitting while retaining important model features.

## 4. Dropout

Dropout is a regularization technique, not a function, specifically designed for neural networks. It involves randomly dropping out (setting to zero) a random subset of the neurons with a certain probability p_{dropout} (typically 0.5) during training, preventing them from contributing to the forward or backward pass.

Dropout forces the neural network to learn to make predictions without relying too heavily on specific neurons, which helps to prevent overfitting and improves generalization.

## 5. Early Stopping

Early stopping is a regularization technique that monitors the model’s performance on a validation dataset during training. If the validation loss starts to increase, training is stopped, preventing the model from overfitting to the training data. By stopping the training process before overfitting occurs, early stopping helps ensure that the model is generalizable to unseen data.

The main idea behind early stopping is to find an optimal point in the training process where the model has learned enough from the training data but has yet to start to overfit.

## Conclusion

In this blog, we have learned about 5 techniques for avoiding model overfitting during the training process. By understanding these techniques, you will be able to build more robust and generalizable models, leading to improved performance on unseen data.