RegressionCatalog.CrossValidate Method

Definition

Namespace:

Microsoft.ML

Assembly:

Microsoft.ML.Data.dll

Package:

Microsoft.ML v3.0.1

Run cross-validation over numberOfFolds folds of data, by fitting estimator, and respecting samplingKeyColumnName if provided. Then evaluate each sub-model against labelColumnName and return metrics.

public System.Collections.Generic.IReadOnlyList<Microsoft.ML.TrainCatalogBase.CrossValidationResult<Microsoft.ML.Data.RegressionMetrics>> CrossValidate (Microsoft.ML.IDataView data, Microsoft.ML.IEstimator<Microsoft.ML.ITransformer> estimator, int numberOfFolds = 5, string labelColumnName = "Label", string samplingKeyColumnName = default, int? seed = default);

member this.CrossValidate : Microsoft.ML.IDataView * Microsoft.ML.IEstimator<Microsoft.ML.ITransformer> * int * string * string * Nullable<int> -> System.Collections.Generic.IReadOnlyList<Microsoft.ML.TrainCatalogBase.CrossValidationResult<Microsoft.ML.Data.RegressionMetrics>>

Public Function CrossValidate (data As IDataView, estimator As IEstimator(Of ITransformer), Optional numberOfFolds As Integer = 5, Optional labelColumnName As String = "Label", Optional samplingKeyColumnName As String = Nothing, Optional seed As Nullable(Of Integer) = Nothing) As IReadOnlyList(Of TrainCatalogBase.CrossValidationResult(Of RegressionMetrics))

Parameters

data

IDataView

The data to run cross-validation on.

estimator

IEstimator<ITransformer>

The estimator to fit.

numberOfFolds

Int32

Number of cross-validation folds.

labelColumnName

String

The label column (for evaluation).

samplingKeyColumnName

String

Name of a column to use for grouping rows. If two examples share the same value of the samplingKeyColumnName, they are guaranteed to appear in the same subset (train or test). This can be used to ensure no label leakage from the train to the test set. If null no row grouping will be performed.

seed

Nullable<Int32>

Seed for the random number generator used to select rows for cross-validation folds.

Returns

IReadOnlyList<CrossValidationResult<RegressionMetrics>>

Per-fold results: metrics, models, scored datasets.