Visible to Intel only — GUID: GUID-62BA60BE-9FD1-4033-B3BF-D00C0A353AC5
Visible to Intel only — GUID: GUID-62BA60BE-9FD1-4033-B3BF-D00C0A353AC5
Linear Regression
Linear regression is a method to model the connection between a dependent variable, which can be a vector, and one or more explanatory variables. This is done by fitting the linear equations to the observed data.
Operation |
Computational methods |
Programming Interface |
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Mathematical Formulation
Training
Given a set of training data, . The X matrix of the size contains , , observations of independent variables.
To estimate the coefficients , you can use the Normal Equation method.
Partial Training
Given a block of training data, . The X matrix of size contains , , observations of independent variables.
To compute a cross product of Linear Regression, you can use the Normal Equation method.
Finalize Training
Given a partial result with partial products.
To compute a cross product of Linear Regression, you can use the Normal Equation method.
Inference
The linear regression-based prediction is made for the input vector using the equation for each .
Computation Method: norm_eq
The method computes means, variance-covariance, or correlation matrix for the dense data. This is the default and the only supported method.
Programming Interface
Refer to API Reference: Linear Regression.
Online mode
The algorithm supports online mode.
Distributed mode
The algorithm supports distributed execution in SPMD mode (only on GPU).