CalibratorEstimatorBase<TICalibrator> Class
Definition
Important
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Base class for calibrator estimators.
public abstract class CalibratorEstimatorBase<TICalibrator> : Microsoft.ML.IEstimator<Microsoft.ML.Calibrators.CalibratorTransformer<TICalibrator>> where TICalibrator : class, ICalibratortype CalibratorEstimatorBase<'ICalibrator (requires 'ICalibrator : null and 'ICalibrator :> ICalibrator)> = class
interface IEstimator<CalibratorTransformer<'ICalibrator>>Public MustInherit Class CalibratorEstimatorBase(Of TICalibrator)
Implements IEstimator(Of CalibratorTransformer(Of TICalibrator))Type Parameters
- TICalibrator
- Inheritance
-
CalibratorEstimatorBase<TICalibrator>
- Derived
- Implements
-
IEstimator<CalibratorTransformer<TICalibrator>>
Remarks
CalibratorEstimators take an IDataView (the output of a Microsoft.ML.Data.BinaryClassifierScorer) that contains a "Score" column, and converts the scores to probabilities(through binning, interpolation etc.), based on the TICalibrator type. They are used in pipelines where the binary classifier produces non-calibrated scores.
Methods
| Fit(IDataView) |
Fits the scored IDataView creating a CalibratorTransformer<TICalibrator> that can transform the data by adding a Microsoft.ML.Data.DefaultColumnNames.Probability column containing the calibrated Microsoft.ML.Data.DefaultColumnNames.Score. |
Explicit Interface Implementations
| IEstimator<CalibratorTransformer<TICalibrator>>.GetOutputSchema(SchemaShape) |
Gets the output SchemaShape of the IDataView after fitting the calibrator. Fitting the calibrator will add a column named "Probability" to the schema. If you already had such a column, a new one will be added. The same annotation data that would be produced by Microsoft.ML.Data.AnnotationUtils.GetTrainerOutputAnnotation(System.Boolean) is marked as being present on the output, if it is present on the input score column. |
Extension Methods
| AppendCacheCheckpoint<TTrans>(IEstimator<TTrans>, IHostEnvironment) |
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. |
| WithOnFitDelegate<TTransformer>(IEstimator<TTransformer>, Action<TTransformer>) |
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. |