Lasso Regression

Lasso Regression
Description	Lasso Regression is used to penalize the regression method to select a subset of variables by imposing a constraint on model parameters.
Why to use	Predictive Modeling
When to use	For variables having high multicollinearity	When not to use	On textual data.
Prerequisites	If the data contains any missing values, use Missing Value Imputation before proceeding with Lasso Regression. If the input variable is of categorical type, use Label Encoder. The output variable must be a continuous data type. Linearity – The relationship between the dependent and independent variables is linear. Independence – The variables should be independent of each other. Normality – The variables should be normally distributed. The Dependent variable (Y) vs. Residuals plot must not follow a pattern. The errors should be normally distributed.
Input	Any continuous data	Output	The predicted value of the dependent variable
Statistical Methods used	Dimensionality Reduction	Limitations	It cannot be used on textual data.

Lasso is the abbreviation of Least Absolute Shrinkage and Selection Operator. It is a regression analysis method that uses shrinkage to perform both variable selection and regularization. This usage of shrinkage increases the prediction accuracy and interpretability of a statistical model.
Shrinkage refers to the fact that the data values shrink towards a central point, like the mean. Lasso regression performs L1 regularization in which a penalty equal to the absolute value of the magnitude of coefficients is added. In this case, some coefficients become zero and are eliminated from the model. Also, heavy penalties result in coefficients with values close to zero. This produces simpler models that are easy to analyze.
Thus, Lasso regression encourages simple and sparse models, where models have a smaller number of parameters. It is suitable for models that show great collinearity to automate variable selection or parameter elimination in a model selection.