Regularization in Machine Learning#Regularization in Machine Learning penalizes the weight when it is too large.
L2 Regularization formula is just the square Euclidean norm of the prime to vector w which is called L^2 Regularization which is the most common.
Two popular examples of Regularization methods for Linear Regression are:Ordinary Least Squares would cause Overfitting the Training dataset.
Misc Notes#The bias is generally not Regularization.
In Python lambda is a reserved word so often it is used as lambd.
More Information#There might be more information for this subject on one of the following:
- Dropout Regularization
- Least Absolute Shrinkage and Selection Operator
- Machine Learning Algorithms
- Ridge Regression