Collection of common data science terms, tools, and concepts with definitions, as assembled by Vincent Granville in an analyticbridge blog post. (accessed 07/24/2014)
The method preferred by statisticians for determining which variables to include in a model. It is a modified version of R^2 which penalizes each new variable on the basis of how many have already been admitted. Due to its construct, R^2 will always increase as you add new variables, which result in models that over-fit the data and have poor predictive ability. Adjusted R^2 results in more parsimonious models that admit new variables only if the improvement in fit is larger than the penalty, which improves the ultimate goal of out-of-sample prediction. (Submitted by Santiago Perez)
