Boosting is a technique in machine learning that has been shown to produce models with high predictive accuracy. One of the most common ways to implement boosting in practice is to use XGBoost, short for "extreme gradient boosting." This tutorial provides a step-by-step example of how to use XGBoost to fit a boosted model in R. For this example we'll fit a boosted regression model to the Boston dataset from the MASS package. This dataset contains 13 predictor variables that we'll use to predict one response variable called mdev, which represents the median value of homes in different census tracts around Boston. We can see that the dataset contains 506 observations and 14 total variables.

Clustering is a technique in machine learning that attempts to find clusters of observations within a dataset. The goal is to find clusters such that the observations within each cluster are quite similar to each other, while observations in different clusters are quite different from each other. Clustering is a form of unsupervised learning because we're simply attempting to find structure within a dataset rather than predicting the value of some response variable. When this information is available, clustering can be used to identify households that are similar and may be more likely to purchase certain products or respond better to a certain type of advertising. One of the most common forms of clustering is known as k-means clustering.