Ridge and lasso regression are supervised linear regression ML algorithms. Both of these algorithms aim at reducing model complexity and prevent overfitting. When there is a large number of features or variables in a training dataset, the model built by ML generally tends to be complex.
The key characteristics of ridge regression are as follows:
- Coefficient shrinkage: This helps in reducing model complexity
- Regularization: This adds information to prevent overfitting