# ComputeLogisticRegressionStandardDeviation Class

## Definition

Computes the standard deviation matrix of each of the non-zero training weights, needed to calculate further the standard deviation, p-value and z-Score. Use this class' implementation in the Microsoft.ML.Mkl.Components package which uses Intel Math Kernel Library. Due to the existence of regularization, an approximation is used to compute the variances of the trained linear coefficients.

public abstract class ComputeLogisticRegressionStandardDeviation
type ComputeLogisticRegressionStandardDeviation = class
Public MustInherit Class ComputeLogisticRegressionStandardDeviation
Inheritance
ComputeLogisticRegressionStandardDeviation
Derived

## Methods

 Computes the standard deviation matrix of each of the non-zero training weights, needed to calculate further the standard deviation, p-value and z-Score. The calculations are not part of Microsoft.ML package, due to the size of MKL. If you need these calculations, add the Microsoft.ML.Mkl.Components package, and initialize ComputeStandardDeviation to the ComputeLogisticRegressionStandardDeviation implementation in the Microsoft.ML.Mkl.Components package. Due to the existence of regularization, an approximation is used to compute the variances of the trained linear coefficients.