This node trains a linear model that minimizes the sum of squared distances between the prediction and the target at each of the provided examples, with an added quadratic regularization term. This linear model is applied to each query input to produce a predicted output. ML Regression Linear provides an alternative to inferencing a trained neural network. In situations where a linear model is a good enough approximation, this node provides a simpler alternative to training and inferencing a neural network.

The first input of ML Regression Linear should consist of labeled examples. For example, created using ML Example inside a SOP for loop or read in from a file using ML Example Import. The second input should be a component with same contributions as each input component of the labeled examples. The input/output contributionsby the multi parameters should match the input/target contributions on the ML nodes from the origin labeled examples.

When ML Train Regression and ML Regression Inference is preferred, ML Regression Linear can be useful for verifying the data set was correctly generated. If you give an input component of a labeled example of the data set, ML Regression Linear should return an exact match for the target. This assumes no two labeled examples in the data set have the same input component.

See Machine Learning documentation for more general information.