# OlsModelParameters Class

## Definition

Model parameters for OlsTrainer.

public sealed class OlsModelParameters : Microsoft.ML.Trainers.RegressionModelParameters
type OlsModelParameters = class
inherit RegressionModelParameters
Public NotInheritable Class OlsModelParameters
Inherits RegressionModelParameters
Inheritance

## Fields

 The coefficient of determination. The adjusted coefficient of determination. It is only possible to produce an adjusted R-squared if there are more examples than parameters in the model plus one. If this condition is not met, this value will be NaN.

## Properties

 The predictor's bias term. (Inherited from LinearModelParameters) Whether the model has per parameter statistics. This is false iff StandardErrors, TValues, and PValues are all null. A model may not have per parameter statistics because either there were not more examples than parameters in the model, or because they were explicitly suppressed in training by setting CalculateStatistics to false. p-values corresponding to each of the model standard errors. This is null if and only if HasStatistics is false. The standard error per model parameter, where the first corresponds to the bias, and all subsequent correspond to each weight in turn. This is null if and only if HasStatistics is false. t-Statistic values corresponding to each of the model standard errors. This is null if and only if HasStatistics is false. The predictor's feature weight coefficients. (Inherited from LinearModelParameters)

## Explicit Interface Implementations

 Used to determine the contribution of each feature to the score of an example by FeatureContributionCalculatingTransformer. For linear models, the contribution of a given feature is equal to the product of feature value times the corresponding weight. (Inherited from LinearModelParameters) (Inherited from ModelParametersBase)