# OlsTrainer Class

## Definition

The IEstimator<TTransformer> for training a linear regression model using ordinary least squares (OLS) for estimating the parameters of the linear regression model.

public sealed class OlsTrainer : Microsoft.ML.Trainers.TrainerEstimatorBase<Microsoft.ML.Data.RegressionPredictionTransformer<Microsoft.ML.Trainers.OlsModelParameters>,Microsoft.ML.Trainers.OlsModelParameters>
type OlsTrainer = class
inherit TrainerEstimatorBase<RegressionPredictionTransformer<OlsModelParameters>, OlsModelParameters>
Public NotInheritable Class OlsTrainer
Inherits TrainerEstimatorBase(Of RegressionPredictionTransformer(Of OlsModelParameters), OlsModelParameters)
Inheritance

## Remarks

To create this trainer, use Ols or Ols(Options).

### Input and Output Columns

The input label column data must be Single. The input features column data must be a known-sized vector of Single.

This trainer outputs the following columns:

Output Column Name Column Type Description
Score Single The unbounded score that was predicted by the model.

### Trainer Characteristics

Is normalization required? Yes
Is caching required? No
Required NuGet in addition to Microsoft.ML Microsoft.ML.Mkl.Components
Exportable to ONNX Yes

### Training Algorithm Details

Ordinary least squares (OLS) is a parameterized regression method. It assumes that the conditional mean of the dependent variable follows a linear function of the dependent variables. The regression parameters can be estimated by minimizing the squares of the difference between observed values and the predictions

## Fields

 The feature column that the trainer expects. (Inherited from TrainerEstimatorBase) The label column that the trainer expects. Can be null, which indicates that label is not used for training. (Inherited from TrainerEstimatorBase) The weight column that the trainer expects. Can be null, which indicates that weight is not used for training. (Inherited from TrainerEstimatorBase)

## Methods

 Trains and returns a ITransformer. (Inherited from TrainerEstimatorBase) (Inherited from TrainerEstimatorBase)

## Extension Methods

 Append a 'caching checkpoint' to the estimator chain. This will ensure that the downstream estimators will be trained against cached data. It is helpful to have a caching checkpoint before trainers that take multiple data passes. Given an estimator, return a wrapping object that will call a delegate once Fit(IDataView) is called. It is often important for an estimator to return information about what was fit, which is why the Fit(IDataView) method returns a specifically typed object, rather than just a general ITransformer. However, at the same time, IEstimator are often formed into pipelines with many objects, so we may need to build a chain of estimators via EstimatorChain where the estimator for which we want to get the transformer is buried somewhere in this chain. For that scenario, we can through this method attach a delegate that will be called once fit is called.