<?xml version="1.0" encoding="utf-8"?>

<doc>

<members>

<member name="OLS">

<summary>

Train an OLS regression model.

</summary>

<remarks>

<a href='https://en.wikipedia.org/wiki/Ordinary_least_squares'>Ordinary least squares (OLS)</a> is a parameterized regression method.

It assumes that the conditional mean of the dependent variable follows a linear function of the dependent variables.

The parameters of the regressor can be estimated by minimizing the squares of the difference between observed values and the predictions.

</remarks>

<example>

<code language="csharp">

new OrdinaryLeastSquaresRegressor

{

L2Weight = 0.1,

PerParameterSignificance = false,

NormalizeFeatures = Microsoft.ML.Models.NormalizeOption.Yes

}

</code>

</example>

</member>

<member name="SymSGD">

<summary>

Parallel Stochastic Gradient Descent trainer.

</summary>

<remarks>

<a href='https://en.wikipedia.org/wiki/Stochastic_gradient_descent'>Stochastic gradient descent (SGD)</a> is an interative algorithm

that optimizes a differentiable objective function. <a href='https://arxiv.org/abs/1705.08030'>SYMSGD</a> parallelizes SGD using Sound Combiners.

</remarks>

<example>

<code language="csharp">

new SymSgdBinaryClassifier()

{

NumberOfIterations = 50,

L2Regularization = 0,

Shuffle = true

}

</code>

</example>

</member>

</members>

</doc>