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Regularization can help build generalizable models by adding dropout layers to neural networks. Regularization is one of the most effective ways to prevent overfitting. There are different types of regularization techniques, including:

L1 Regularization (Lasso): This adds a penalty term based on the absolute values of the model's coefficients. It encourages sparsity in the model, effectively selecting a subset of the most important features.

L2 Regularization (Ridge): This adds a penalty term based on the square of the model's coefficients. It prevents the coefficients from becoming too large, which can help reduce overfitting.

Dropout (for neural networks): Dropout is a regularization technique specific to neural networks. During training, random neurons are "dropped out" (i.e., ignored) with a certain probability, which helps prevent the network from relying too heavily on any single neuron.

Early Stopping: This is a technique where the training process is halted when the model's performance on a validation dataset starts to degrade. It prevents the model from continuing to learn and overfit.
As discussed in supportvector machines (SVM), we have,
 [4078a]
where,
g is the activation function.
n is the number of input features.
Equation 4078a is a basic representation of a singlelayer neural network, also known as a perceptron or logistic regression model, depending on the choice of the activation function g. As shown in Table 4078a, from Equation 4078a, we can derive different forms or variations by changing the activation function, the number of layers, or the architecture of the neural network.
The model can be relatively simple when the dataset is small (page3300). However, if we scale up the data, we might also want to explore more complex architectures or additional features like dropout for regularization.
Table 4078a. Different forms or variations of Equation 4078a.
Algorithms 
Without regularization 
With regularization 
Linear Regression 
Set g(z) = z (identity function).
This simplifies the equation to , which is the formula for linear regression.
In linear regression, a goal is to minimize the least squares (OLS) or mean squared error (MSE) term below,

With regularization, the
goal is to minimize the term below,
The second part (λ/2)θ^{2} is the L2 regularization term, where λ is the regularization parameter and θ^{2} is the squared L2 norm of the parameter vector θ.
Here, λ cannot be too large, otherwise, θ is forced to be zero since the second term needs to be close to zero. 
Logistic Regression 
Set g(z) = 1 / (1 + e^{(z)}) (the sigmoid function).
This is a binary classification model, and the equation becomes the logistic regression model.
To find the optimal parameters θ, we typically maximize the loglikelihood, which is the natural logarithm of the likelihood:

To find the optimal parameters θ, we typically maximize the loglikelihood,
This is the regularized loglikelihood function used in regularized logistic regression (also known as L2 regularization or Ridge regularization). In this case, we have an additional term,  λθ^{2}, which is a penalty term on the magnitude of the model parameters θ. This term is used to prevent overfitting by discouraging overly complex models with large parameter values. The λ (lambda) parameter is a hyperparameter that controls the strength of the regularization; larger values of λ lead to stronger regularization. 
Multilayer Neural Network 
You can add more layers to the network by introducing new sets of weights and biases, and applying activation functions at each layer. This leads to a more complex model. 

Different Activation Functions 
You can choose different activation functions for different characteristics of your model. For example, you can use ReLU, tanh, or other nonlinear activation functions instead of the sigmoid function. 

Deep Learning Architectures 
You can create more complex neural network architectures, such as convolutional neural networks (CNNs) for image data or recurrent neural networks (RNNs) for sequential data. 

Regularization 

You can add regularization terms, such as L1 or L2 regularization, to the loss function to prevent overfitting. 
Table 4078b lists the advantages and disadvantages of regularization for avoiding or mitigating overfitting.
Table 4078b. Advantages and disadvantages of regularization for avoiding or mitigating overfitting.
Concept 
Advantages 
Disadvantages 
Regularization techniques like L1 (Lasso) and L2 (Ridge) regularization add penalty terms to the loss function, discouraging the model from assigning too much importance to any one feature or parameter. This one of the most effective ways to prevent overfitting. 
Improved Generalization: Regularization helps improve a model's ability to generalize to unseen data. It encourages the model to focus on the most important features and avoid fitting noise in the training data.
Simplicity: Regularization techniques are relatively easy to implement and do not require major changes to the model architecture. They can be incorporated into existing models with minimal effort.
Reduced Overfitting: Regularization explicitly targets the reduction of overfitting, which is a common problem in machine learning. By adding a penalty term to the loss function, regularization discourages the model from becoming too complex and fitting the training data too closely.
Feature Selection: Some forms of regularization, like L1 regularization (Lasso), encourage sparsity in the model's coefficients. This means that they can perform implicit feature selection by driving some feature weights to zero, effectively identifying the most relevant features. 
Hyperparameter Tuning: Regularization introduces hyperparameters (e.g., the strength of regularization) that need to be tuned. Finding the right hyperparameters can be challenging and timeconsuming, as it often requires experimentation.
Loss of Expressiveness: Overly aggressive regularization can lead to underfitting, where the model is too simple and cannot capture the underlying patterns in the data. Balancing the right amount of regularization is crucial.
Computational Overhead: Some regularization techniques, such as dropout in neural networks, require additional computational resources during training, which can slow down the training process.
Not a OneSizeFitsAll Solution: The choice of which regularization method to use depends on the problem and the characteristics of the data. There is no onesizefitsall solution, and the best regularization technique may vary from one problem to another.
Interpretability: Regularized models may be less interpretable than nonregularized models, especially when L1 regularization is used to induce sparsity. It can be harder to understand the importance of individual features in the model. 
It is also possible to apply regularization techniques to individual elements of model parameters, especially in neural networks and deep learning. The choice of regularization technique depends on the problem at hand and the characteristics of your data and model. It's common to experiment with different regularization methods and hyperparameters to find the best regularization strategy for your specific task. Regularization helps improve the generalization performance of your model by reducing the risk of overfitting. Table 4078c lists the common regularization techniques for neural network parameters.
Table 4078c. Common regularization techniques for neural network parameters.
Regularization 
Details 
L1 Regularization (Lasso) 
L1 regularization encourages sparse weight vectors by adding the absolute values of the weights as a penalty to the loss function. This can lead to some weights becoming exactly zero, effectively pruning the network. 
L2 Regularization (Ridge) 
L2 regularization adds the squared values of the weights as a penalty to the loss function. It encourages smaller weight values and helps prevent large weight magnitudes that could lead to overfitting. 
Elastic Net Regularization 
Elastic Net is a combination of L1 and L2 regularization, allowing you to apply both penalties simultaneously. It can be useful when you want a balance between sparsity and weight decay. 
Weight Decay 
Weight decay is another term for L2 regularization, and it refers to the practice of adding the L2 penalty to the loss function. This helps control the magnitude of the weights. 
Group Lasso 
Group Lasso is a form of L1 regularization applied to groups of related parameters. It encourages sparsity among groups of parameters rather than individual weights. This is often used when there is some inherent grouping or structure in the parameters, like convolutional filters in a CNN. 
DropConnect 
Similar to dropout, DropConnect randomly sets individual weight connections to zero during training, which regularizes the network by making it less reliant on specific weights. 
Weight Clipping 
Weight clipping involves bounding the values of weights to a specific range during training. This can help prevent the growth of excessively large weights and mitigate exploding gradient problems. 
Figure 4078a shows a comparison between variances with and without regularization. The variance represents the mean squared error (MSE) between the model predictions and the actual data points. Both cases used random distributed data as dataset. The variances for both cases without and with regularization are 0.71 and 0.67, respectively. The variance without regularization (alpha=0) is slightly higher than the variance with regularization (alpha=1). However, in some cases, the difference is quite small so that the effect of regularization may vary depending on the dataset and the specific parameters. Figure 4078b shows the comparison between bias without and with regularization.
Figure 4078a. Comparison between variances without and with regularization. (code)
Figure 4078b shows the comparison between bias without and with regularization.
Figure 4078b. Comparison between bias without and with regularization. (code)
Regularization tends to reduce overfitting, which means it helps in reducing variance rather than bias. While regularization might slightly increase the bias in some cases due to the penalty on complex models, the primary purpose of regularization is to control variance and improve the model's generalization to new data.
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