BinaryClassificationCatalog Class

Definition

Namespace:

Microsoft.ML

Assembly:

Microsoft.ML.Data.dll

Package:

Microsoft.ML v4.0.0-preview.24522.7

Source:

TrainCatalog.cs

Class used by MLContext to create instances of binary classification components, such as trainers and calibrators.

C#

public sealed class BinaryClassificationCatalog : Microsoft.ML.TrainCatalogBase

Inheritance

Object

TrainCatalogBase

BinaryClassificationCatalog

Properties

Calibrators	The list of calibrators for performing binary classification.
Trainers	The list of trainers for performing binary classification.

Methods

ChangeModelThreshold<TModel>(BinaryPredictionTransformer<TModel>, Single)	Method to modify the threshold to existing model and return modified model.
CrossValidate(IDataView, IEstimator<ITransformer>, Int32, String, String, Nullable<Int32>)	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 CalibratedBinaryClassificationMetrics object, which includes probability-based metrics, for each sub-model. Each sub-model is evaluated on the cross-validation fold that it did not see during training.
CrossValidateNonCalibrated(IDataView, IEstimator<ITransformer>, Int32, String, String, Nullable<Int32>)	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.
Evaluate(IDataView, String, String, String, String)	Evaluates scored binary classification data.
EvaluateNonCalibrated(IDataView, String, String, String)	Evaluates scored binary classification data, without probability-based metrics.

Extension Methods

PermutationFeatureImportance<TModel>(BinaryClassificationCatalog, ISingleFeaturePredictionTransformer<TModel>, IDataView, String, Boolean, Nullable<Int32>, Int32)	Permutation Feature Importance (PFI) for Binary Classification.
PermutationFeatureImportanceNonCalibrated(BinaryClassificationCatalog, ITransformer, IDataView, String, Boolean, Nullable<Int32>, Int32)	Permutation Feature Importance (PFI) for Binary Classification.

Applies to