A common concern in predictive modeling is whether a model has been overfit. In statistics, overfitting refers to the phenomena when an analytical model corresponds too closely (or exactly) to a specific data set, and therefore may fail when applied to additional data or future observations. One common method that can be used to mitigate overfitting is regularization. Regularization places controls on how large the coefficients of the predictor variables grow. In Alteryx, the option of implementing regularized regression is available for the Linear Regression and Logistic Regression Tools.
View full article