BinaryClassificationCatalog Class

Definition

Namespace:

Microsoft.ML

Assembly:

Microsoft.ML.Data.dll

Package:

Microsoft.ML v3.0.1

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

public sealed class BinaryClassificationCatalog : Microsoft.ML.TrainCatalogBase

type BinaryClassificationCatalog = class
    inherit TrainCatalogBase

Public NotInheritable Class BinaryClassificationCatalog
Inherits 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.