L1-LASSO vs Linear SVM

What is linear SVM?

A linear Support Vector Machine (SVM) is a supervised learning algorithm used for classification tasks. It works by determining the best hyperplane in feature space to divide data points belonging to various classes. The margin, or the distance between the hyperplane and the closest data point from each class (referred to as support vectors), is maximum when this particular hyperplane is selected....

What is L1-LASSO?

L1-Regularized Least Absolute Shrinkage and Selection Operator (LASSO) is a regression technique used for feature selection and regularization in linear regression models. L1 regularization, commonly known as LASSO, adds a penalty term to the standard linear regression objective function, which penalizes the absolute values of the regression coefficients....

L1-LASSO vs Linear SVM

Feature L1-LASSO Linear SVM Optimization Objective Minimize loss function + L1 regularization Maximize margin between classes Type of Algorithm Regression Classification Decision Boundary N/A Hyperplane Feature Selection Yes, automatically selects features by shrinking coefficients to zero No direct feature selection mechanism, but can indirectly indicate feature importance Regularization Yes, through L1 regularization Can incorporate regularization, often L2 regularization for soft margin SVM Sparsity Promotes sparsity in coefficient vector Does not inherently promote sparsity Application Feature selection, regression with high-dimensional data Binary and multiclass classification, often used for linearly separable data Computational Efficiency May require significant computation due to iterative optimization Efficient, particularly in high-dimensional space, as it depends only on support vectors Interpretable Yes, due to feature selection aspect Generally less interpretable due to lack of feature selection mechanism Sensitivity to Outliers Sensitive, as outliers can affect coefficients Generally less sensitive due to focus on margin rather than individual data points...

When to use L1-LASSO and linear SVM ?

The choice between L1-LASSO and linear SVM depends on various factors such as the nature of the data, the specific task at hand, and the desired outcome....