What is Lasso Regression?
Lasso regression, also known as the Least Absolute Shrinkage and Selection Operator, is a type of linear regression that uses shrinkage. Shrinkage is where data values are shrunk towards a central point, like the mean. The lasso procedure encourages simple, sparse models (i.e., models with fewer parameters). This particular type of regression is well-suited for models showing high levels of multicollinearity or when you want to automate certain parts of model selection, like variable selection/parameter elimination.
The Lasso Regression Formula
The lasso regression performs both variable selection and regularization in order to enhance the prediction accuracy and interpretability of the statistical model it produces. The formula for lasso regression is:
Minimize (1/2n) * ||Y - Xw||^2 + λ * ||w||1
How Lasso Regression Works
Lasso regression works by adding a penalty equivalent to the absolute value of the magnitude of coefficients. This type of regularization (L1) can lead to zero coefficients in the model (some coefficients can become exactly zero), which is a form of automatic feature selection. Since some coefficients can become zero, you end up with fewer features in the final model. The λ parameter controls the impact of the penalty, and thus the level of feature selection: when λ = 0, lasso regression produces the same coefficients as a linear regression. When λ is very large, all coefficients are shrunk to zero.
The main benefit of lasso regression, and a key difference from ridge regression, is that it can produce simpler and more interpretable models that incorporate only a subset of the predictors. This is particularly useful when you have a large set of predictors and want to automatically select a subset to use in the final model.
Choosing the Tuning Parameter
The tuning parameter λ controls the strength of the penalty term. The value of λ can be chosen using cross-validation, where different values of λ are tested and the one that results in the lowest prediction error is selected.
Advantages of Lasso Regression
- Feature Selection: By penalizing the absolute size of coefficients, lasso drives some coefficients to zero, effectively selecting a simpler model that does not include those coefficients.
- Interpretability: A model with fewer parameters is generally easier to interpret.
- Handling Multicollinearity: Lasso can handle multicollinearity (high correlations among predictors) better than ridge regression, as it will include only one variable and drive the others to zero.
- Model Complexity: Lasso regression can provide a more parsimonious model, reducing the complexity of the final model.
Disadvantages of Lasso Regression
- Selection of Tuning Parameters: The need to choose a tuning parameter λ can be seen as a disadvantage, as it adds complexity to the model.
- Unstable Selection: Lasso might not be consistent, and the inclusion of variables can depend heavily on model tuning.
- Difficulty with Large Number of Predictors: When the number of predictors is greater than the number of observations, lasso will select at most n predictors as non-zero, even if all predictors are relevant (or "true").
Applications of Lasso Regression
Lasso regression is widely used in the field of machine learning and statistics for:
- Feature Selection: Identifying significant predictors from a large set of potential variables.
- Building Predictive Models: Developing models when the goal is prediction and interpretation in the presence of many features.
- Compressed Sensing: Efficiently acquiring and reconstructing a signal from a small number of samples.
Lasso Regression History
Lasso regression was originally introduced in 1996 by Robert Tibshirani, who developed it from the perspective of geometry and constraint optimization. It has become a cornerstone of high-dimensional statistical modeling, where the number of predictors p can be larger than the number of observations n.
Lasso regression is a powerful technique that performs regularization and feature selection to improve the prediction accuracy and interpretability of statistical models. It is particularly useful when dealing with complex datasets with many features, and where some form of feature selection is desired.