What Regularization Meaning, Applications & Example
A technique to prevent overfitting by adding a penalty term.
What is Regularization?
Regularization is a technique used in machine learning and statistics to prevent overfitting by adding a penalty to the model ’s complexity. It helps the model generalize better to unseen data by discouraging overly complex models that fit the noise in the training data. Regularization methods adjust the learning process to find a balance between fitting the data well and keeping the model simpler.
Types of Regularization
- L1 Regularization (Lasso): Adds a penalty proportional to the absolute value of the coefficients. It can lead to sparse models, where some coefficients are exactly zero, effectively performing feature selection .
- L2 Regularization (Ridge): Adds a penalty proportional to the square of the coefficients. It reduces the size of the coefficients but generally does not make them exactly zero, helping to prevent large weights.
- Elastic Net: Combines both L1 and L2 regularization. It’s useful when there are multiple correlated features in the data, as it can handle them better than L1 or L2 alone.
Applications of Regularization
- Preventing Overfitting: Regularization is widely used in linear and logistic regression to avoid overfitting, particularly when the number of features is large compared to the number of samples.
- Neural Networks: In deep learning , regularization techniques like dropout and weight decay help prevent overfitting, especially when training large, complex models.
- Time Series Forecasting: Regularization can improve predictive accuracy by preventing a model from overfitting to noise in the historical data.
Example of Regularization
In linear regression, applying L2 regularization (Ridge regression) would add a penalty term to the cost function, forcing the model to keep the coefficients small. This helps prevent the model from fitting noise in the training data, leading to better performance on unseen data. For instance, if you have a regression model predicting house prices and the data includes many correlated features (e.g., square footage, number of rooms), L2 regularization can reduce the impact of less relevant features, ensuring a more generalizable model.