regularization

In this post we will talk about the Bias-Variance tradeoff, explaining where it comes from and how we can manage it, introducing techniques for model selection (feature selection, regularization, dimensionality reduction) and model ensemble (bagging and boosting). Disclaimer: the following notes were written following the slides provided by the professor Restelli at Polytechnic of Milan and the book ‘Pattern Recognition and Machine Learning'. Bias-Variance trade-off and Model Selection No Free Lunch Theorems Define $Acc_G(L)$ as the generalization accuracy of the learner $L$, which is the accuracy of $L$ on non-training samples....

In this post we will analyze Linear Regression Models in a pretty much detailed way, discussing the different approaches in which the problem can be tackled and also explaining what is regularization. Disclaimer: the following notes were written following the slides provided by the professor Restelli at Polytechnic of Milan and the book ‘Pattern Recognition and Machine Learning'. The goal of regression is to predict the value of one or more continuous target variables $t$ given the value of a D-dimensional vector $\boldsymbol{x}$ of input variables....