3 Questions
Explain the purpose of Lasso regression and how it differs from ordinary linear regression.
Lasso regression adds a penalty term based on the absolute values of the coefficients, encouraging sparsity in the model by shrinking some coefficients to zero. This is different from ordinary linear regression, which does not impose a penalty for coefficient size.
What is the effect of the penalty term in Lasso regression?
The penalty term in Lasso regression encourages sparsity in the model, effectively shrinking some coefficients to exactly zero, leading to a simpler and more interpretable model.
How does L1 regularization, or Lasso regression, contribute to feature selection?
L1 regularization in Lasso regression encourages sparsity in the model, leading to automatic feature selection by effectively setting some coefficients to zero, thus selecting only the most relevant features for the model.
Test your knowledge of Lasso regression and its application in linear regression models with this quiz. Explore the concepts of L1 regularization, coefficient sparsity, and the impact of penalty terms on model coefficients.
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