Machine Learning, Data Science, Data, Artificial Intelligence, Coding.
selection algorithms. iii) Regularization : Keep all the features, but reduce the magnitude of parameters θ​. Regularization works well when we have a lot of slightly useful features. And it’s opposite Underfitting is a problem as well :) Underfitting occurs when the model is too simple and fails