Developer Guide for Intel® Data Analytics Acceleration Library 2019 Update 5
LASSO algorithm follows the general workflow described in Usage Model: Training and Prediction.
For a description of common input and output parameters, refer to Usage Model: Training and Prediction. The LASSO algorithm has the following input parameters in addition to the common input parameters:
Input ID |
Input |
---|---|
weights |
Optional input. Pointer to the 1 x n numeric table with weights of samples. The input can be an object of any class derived from NumericTable except for PackedTriangularMatrix, PackedSymmetricMatrix, and CSRNumericTable. By default, all weights are equal to 1. |
gramMatrix |
Optional input. Pointer to the p x p numeric table with pre-computed Gram Matrix. The input can be an object of any class derived from NumericTable except for CSRNumericTable. By default, the table is set to an empty numeric table. It is used only when the number of features is less than the number of observations. |
The LASSO batch training algorithm has the following parameters:
In additional to linear regression result LASSO algorithm has the following optional results:
For a description of the input and output, refer to Usage Model: Training and Prediction.
At the prediction stage, LASSO algorithm has the following parameters:
C++:
Java*:
Python*: