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)
Input and Output Columns
This trainer outputs the following columns:
|Output Column Name||Column Type||Description|
||Single||The unbounded score that was predicted by the model.|
|Machine learning task||Regression|
|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
Check the See Also section for links to usage examples.
The feature column that the trainer expects.(Inherited from TrainerEstimatorBase<TTransformer,TModel>)
The label column that the trainer expects. Can be
The weight column that the trainer expects. Can be
Trains and returns a ITransformer.(Inherited from TrainerEstimatorBase<TTransformer,TModel>)
|GetOutputSchema(SchemaShape)||(Inherited from TrainerEstimatorBase<TTransformer,TModel>)|
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<TTransformer> are often formed into pipelines with many objects, so we may need to build a chain of estimators via EstimatorChain<TLastTransformer> 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.