Lasso regression is a popular feature selection method that has been widely used in machine learning, statistics, and electrical engineering. It is a type of linear regression that uses L1 regularization to shrink the coefficients of less important features to zero. This results in a sparse model that only includes the most relevant features, making it easier to interpret and more computationally efficient.
One of the main advantages of Lasso regression is its ability to handle high-dimensional data with a small number of observations. It is particularly useful when dealing with datasets that have a large number of features but only a small number of samples. In addition, Lasso regression has been shown to have robustness properties, making it more resistant to noise and outliers in the data. However, it is important to be careful when interpreting the selected features, as Lasso regression is sensitive to correlations between features and the result depends on the algorithm used.
What is Lasso Regression?
Definition of Lasso Regression
Lasso regression, also known as L1 regularization, is a statistical method used for feature selection and regularization in linear regression models. It was introduced by Robert Tibshirani in 1996 as an extension of the LARS (Least Angle Regression) algorithm.
Lasso regression is similar to ridge regression, but instead of adding a penalty term for the sum of the squares of the coefficients, it adds a penalty term for the sum of the absolute values of the coefficients. This results in a sparse model, where some of the coefficients are set to zero, and only the most important features are retained.
Why is Lasso Regression Important?
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