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Can lasso be used for binary classification?
1 Answer. It is valid. Note the family=”binomial” argument which is appropriate for a classification problem. A normal lasso regression problem would use the gaussian link function.
Can you use categorical variables in lasso?
This allows you to deal with multiple categorical variables without identifiability problems. In answer to your specific questions: (1) LASSO is an estimation method for the coefficients, but the coefficients themselves are defined by the initial model equation for your regression.
Can lasso take factor variables?
In this sense, Lasso is a proper method for factor data analysis, as it takes care of difficulties described above. However, the variable selection property of Lasso yields a new problem: partial selection of dummy variables.
What is the cost function of Ridge and Lasso regression?
1 Cost function of Ridge and Lasso regression and importance of regularization term. 2 Went through some examples using simple data-sets to understand Linear regression as a limiting case for both Lasso and Ridge regression. 3 Understood why Lasso regression can lead to feature selection whereas Ridge can only shrink coefficients close to zero.
What does Lasso mean for categorical variables in regression?
That means that if one of these dummies is included, your model now has a parameter whose interpretation is “the difference between one level of this factor and an arbitrarily chosen other level of that factor”. And maybe none of the other dummies for that factor were selected.
Can a lasso be used with factor variables?
I know that having factor variables doesn’t really work in LASSO through either lars or glmnet, but the variables are too many and there are too many different, unordered values they can take on to reasonably recode them numerically. Can LASSO be used in this situation? How do I do this?
How does ridge regression help reduce model complexity?
So, ridge regression shrinks the coefficients and it helps to reduce the model complexity and multi-collinearity. Going back to eq. 1.3 one can see that when λ → 0 , the cost function becomes similar to the linear regression cost function (eq. 1.2).