BinaryClassificationCatalog.CrossValidateNonCalibrated Method
Definition
Important
Some information relates to prerelease product that may be substantially modified before it’s released. Microsoft makes no warranties, express or implied, with respect to the information provided here.
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 a BinaryClassificationMetrics object, which
do not include probability-based metrics, for each sub-model. Each sub-model is evaluated on the cross-validation fold that it did not see during training.
public System.Collections.Generic.IReadOnlyList<Microsoft.ML.TrainCatalogBase.CrossValidationResult<Microsoft.ML.Data.BinaryClassificationMetrics>> CrossValidateNonCalibrated (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.CrossValidateNonCalibrated : 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.BinaryClassificationMetrics>>Public Function CrossValidateNonCalibrated (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 BinaryClassificationMetrics))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 for the random number generator used to select rows for cross-validation folds.
Returns
Per-fold results: metrics, models, scored datasets.