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Spline Model Tool

The Spline Model tool provides the multivariate adaptive regression splines (or MARS) algorithm of Friedman.* This method is a modern statistical learning model that: (1) self-determines which subset of fields best predict a target field of interest; (2) is able to capture highly nonlinear relationships and interactions between fields; and can automatically address a broad range of regression and classification problems in a way that can be transparent to the user (the user can do as little as specify a target field and a set of predictor fields, but the tool can be extensively fine-tuned by advanced users).

The tool is applicable to a wide range of problems, such as classification, count data, and continuous target regression problems. The method uses a two-step approach to develop a model. In the first step (known as the forward pass, and is similar to the recursive partitioning algorithm used in the Decision Tree tool) the variables that matter most in predicting the target determined, and appropriate "split points" (known as "knots") in the variables are found. However, unlike in a decision tree, a line between adjacent knots (called a term) is fit rather than using discrete jumps as is done in decision trees. This results in a the construction of a piecewise linear function for each variable that can closely approximate any relationship between the target and the predictor variables. The second step (known as the backward or pruning pass) removes some of the knots in the variables (elongating the line segment in the remaining terms) in order to minimize the chance that the model is overfitting the estimation sample and capturing estimation sample noise as opposed to the underlying signal.

This tool uses the R programming language. Go to Options > Download Predictive Tools to install R and the packages used by the R Tool.

Input

An Alteryx data stream that includes a target field of interest along with one or more possible predictor fields.

Configuration Properties

Required Parameters

Model Customization (optional)

Graphics Options

Output

*https://en.wikipedia.org/wiki/Multivariate_adaptive_regression_splines

**Freidman, Jerome H., "Multivariate Adaptive Regression Splines", Stanford University, August 1990

***Freidman, Jerome H., "Fast MARS", Technical Report No. 102, Department of Statistics, Stanford University, May 1993