Recommendation System in R. Recommender systems are used to predict the best products to offer to customers.

These babies have become extremely popular in virtually every single industry, helping customers find products they'll like. Most people are familiar with the idea, but nearly everyone is exposed to several forms of personalized offers and recommendations each day (Google search ads being among the biggest source). Building recommendation systems is part science, part art, and many have become extremely sophisticated. Such a system might seem daunting for those uninitiated, but it's actually fairly straight forward to get started if you're using the right tools. This is a post about building recommender systems in R. ‘Variable Importance Plot’ and Variable Selection. Classification trees are nice.

They provide an interesting alternative to a logistic regression. I started to include them in my courses maybe 7 or 8 years ago. Targeted Learning R Packages for Causal Inference and Machine Learning. By Sherri RoseAssistant Professor of Health Care PolicyHarvard Medical School Targeted learning methods build machine-learning-based estimators of parameters defined as features of the probability distribution of the data, while also providing influence-curve or bootstrap-based confidence internals.

The theory offers a general template for creating targeted maximum likelihood estimators for a data structure, nonparametric or semiparametric statistical model, and parameter mapping. These estimators of causal inference parameters are double robust and have a variety of other desirable statistical properties. Targeted maximum likelihood estimation built on the loss-based “super learning” system such that lower-dimensional parameters could be targeted (e.g., a marginal causal effect); the remaining bias for the (low-dimensional) target feature of the probability distribution was removed. Of key importance are the two R packages SuperLearner and tmle.