Datanice – Machine Learning 101 : What is regularization ? [Interactive]

In Machine learning and statistics, a common task is to fit a model to a set of training data. This model can be used later to make predictions or classify new data points. When the model fits the training data but does not have a good predicting performance and generalization power, we have an overfitting problem. Regularization is a technique used to avoid this overfitting problem. The idea behind regularization is that models that overfit the data are complex models that have for example too many parameters.