Machine learning regularization is a vital concept that can help you improve model performance and robustness when you’re training with complex datasets. It addresses one of the most common challenges faced by machine learning models: also known as overfitting: training a model that does very well on the training data, but doesn’t generalize to new, unseen data. In the following article, we’ll discuss what regularization is in machine learning, why it’s needed, and some most popular ways to apply regularization.
In this article let’s deep dive into What Is Regularization in Machine Learning.
In a nutshell, regularization adds some extra constraints or some type of penalty on a machine learning model’s parameters (weights), in order to make it ‘become’ simpler. Regularization, by doing that, encourages the model to attend less on the training data and to generalize better to unseen data.
In layman’s terms, regularization is to prevent models from being ‘too optimistic’ about the training data. If a model is too complex, it might latch onto the noise in the data and start fitting ‘patterns’ which can’t be applied to new data and in fact perform poorly on unseen data. To avoid this regularization penalizes large weights or coefficients which could cause overfit.
Why is this important?
1. Prevents Overfitting
When a model learns the noise along with the genuine patterns in the data that’s called overfitting. Therefore, the model is too specific to training data and works badly on the new data. By adding a penalty to complex models, this prevents that by essentially making them lose the ability to memorize the training data.
2. Improves Generalization
The primary reason to use a machine learning model is to generalize well to unseen data. Regularization is good in case of models to read in a trend of the data instead of unworthy noise. Regularization controls model complexity allowing the model to generalize well on datasets other than the training dataset.
3. Simplifies Models
Regularization also helps making the model more simple by decreasing the coefficients or weights magnitude. The model stays interpretable, and this is absolutely important, especially when we are working with real-life problems where we have to understand the relationships between the variables.
Different kinds of Regularization Techniques.
One way to apply regularization to a machine learning model is L1 regularization, L2 regularization, Elastic Net regularization and so on. Now let’s get into details of each.
1. Ridge regression – L2 Regularization
L2 regularization also is known as Ridge Regression and is one of the most common forms of regularization in machine learning. Instead, it adds onto the cost function, a penalty term which is squared, multiplied by the magnitude of the coefficients or weights. This penalty term end up shrinking the weights, so that model does not favor one particular feature too much. The formula for L2 regularization is:
Where:
- MSE is the mean squared error
- λ (lambda) is the regularization parameter (controls the strength of regularization)
- w are the model weights
In Ridge Regression smaller λ means that weights get penalized more and the model becomes more simple.
2. Lasso Regression (L1 Regularization)
L1 (or Lasso Regression (Least Absolute Shrinkage and Selection Operator}}, adds a penalty that’s proportional to the absolute value of the magnitude of the coefficients. While Ridge Regression does not produce sparse models, Lasso is able to do so, where some feature coefficients become precisely zero. Due to this, Lasso is a good feature selection technique as it chooses to leave out unimportant or less important features from the model.
The cost function for L1 regularization is:
Lasso produces a more interpretable model because it encourages sparsity, i.e., it gives us the ability to select only some features.
3. Elastic Net Regularization
Lasso and Ridge are both types of regularization; Elastic Net is a combination of both. It combines the pros of the two approaches, and also neutralizes their drawbacks, by bringing in two hyperparameters, one for each type of regularisation. Due to this, Elastic Net works best when there are more features than the amount of data (High-dimensional data), and Ridge or Lasso can ’t be applied independently because of the risk of Multicollinearity and a weak feature selection, and that’s where Ridge and Lasso come to the rescue.
The Elastic Net cost function is:
Where:
- λ1 controls L1 regularization
- λ2 controls L2 regularization
How to Select the Most Suitable Regularization Technique?
When it comes to regularization, whether L1 or L2 or Elastic Net, which one to go for depends on the data and the purpose of the modeling:
- L1 Regularization (Lasso) is handy when you want to consider only important features and get rid of all irrelevant ones.
- L2 Regularization (Ridge) is applicable in situations when all features are likely to play a role in a model, however, it is necessary to curb extreme weight values to reduce overfitting.
- Elastic Net Regularization is helpful, when one wish to have both Lasso and Ridge benefits over the other particularly in high-dimensional data which is prone to multicollinearity.
Other regularization techniques and settings can also be evaluated by cross-validation techniques to enhance model performance.
Conclusion
Regularization is very important in machine learning since it prevents models from overfitting, increases their generalization capability, and reduces their complexity. L1, L2 or Elastic Net Regularization makes no difference in how your model will be able to ‘see’ only the true data patterns and become even more robust for different unseen data. Understanding and applying regularization approaches will help you create better and more explainable machine learning models that will perform better in the real world.
