Lasso Regression:

Lasso regression, short for "Least Absolute Shrinkage and Selection Operator" regression, is a linear regression technique used for variable selection and regularization.


Lasso regression is a technique used in statistics and machine learning to build models when you have lots of input features. It helps simplify the model by automatically selecting the most important features and making some of the less important ones completely irrelevant. This can lead to more accurate and understandable models. It does this by adding a penalty term that encourages the model to set some feature coefficients to zero. So, lasso regression is like a feature selector that helps you focus on what matters most for your prediction while ignoring the rest.

 


Here's what the terms in this equation represent:

  • ‘m’ is the number of training examples.
  • ‘n, is the number of features.
  • ‘hw(x^(i))’ is the predicted value for the ith training example using the linear regression model with weights w.
  • ‘y^(i)’ is the actual target value for the ith training example.
  • ‘Wj’ represents the weight or coefficient associated with the jth feature.
  • λ (lambda) is the regularization parameter, which controls the strength of regularization. A higher value of lambda results in stronger regularization.