Lasso Regularization can be used for variable selection in Linear Regression. true or false
Answers
True
- Less absolute shrinkage and selection operator, also known as lasso or LASSO, is a regression analysis technique used in statistics and machine learning that performs both variable selection and regularisation in order to improve the predictability and understandability of the resulting statistical model. Robert Tibshirani, who originated the phrase, first used it in geophysics and subsequently.
- Lasso was initially developed for models of linear regression. Its linkages to ridge regression, best subset selection, and lasso coefficient estimates and so-called soft thresholding are a few examples. Additionally, it demonstrates that if covariates are collinear, the coefficient estimates do not necessarily need to be unique, unlike in normal linear regression.
- When performing lasso regression, we use an absolute penalty, which causes some coefficients to be zero.
Hence, the statement is true.
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Answer:
The correct answer to the given question is that the given statement is true.
Step-by-step explanation:
In machine learning, the selection is also known as variable selection.
It is defined as the process of selecting relevant features for use in constructing the model.
It is also known as an attribute or variable subset selection.
The Lasso is a Less Attribute shrinkage and selection operator.
It is a regression analysis tool used for variable selection and also it performs regularisation to improve the predictability and understandability of the resulting statistical model.
The lasso regularisation was also used in the concept of statistics and machine learning.
The Lasso was first developed for the models of linear regression.
A few examples are its linkages to ridge regression and best subset selection.
It also explains whether the covariates are collinear.
Therefore, the Lasso Regularization can be used for variable selection in linear regression.
Hence, the given statement is true.
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