The process of identifying the characteristics of a data set which will be most valuable when constructing a model. This is particularly useful with large data sets, as using fewer features will reduce the time and complexity required to train and test a model.
The process begins by measuring the relevance of each characteristic of a dataset to predict your target variable. You then choose a subset of features that will lead to a high-performance model.