Boosting is a technique in machine learning in which multiple models are developed sequentially. Each new model tries to successful predict what prior models were unable to do. The average for regression and majority vote for classification are used. For classification, boosting is commonly associated with decision trees. However, boosting ...

Recommendation engines make future suggestion to a person based on their prior behavior. There are several ways to develop recommendation engines but for purposes, we will be looking at the development of a user-based collaborative filter. This type of filter takes the ratings of others to suggest future items to ...

Elastic net regression combines the power of ridge and lasso regression into one algorithm. What this means is that with elastic net the algorithm can remove weak variables altogether as with lasso or to reduce them to close to zero as with ridge. All of these algorithms are examples of ...