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The IEstimator<TTransformer> for predicting a target using a binary classification model.
public sealed class PriorTrainer : Microsoft.ML.IEstimator<Microsoft.ML.Data.BinaryPredictionTransformer<Microsoft.ML.Trainers.PriorModelParameters>>, Microsoft.ML.Trainers.ITrainerEstimator<Microsoft.ML.Data.BinaryPredictionTransformer<Microsoft.ML.Trainers.PriorModelParameters>,Microsoft.ML.Trainers.PriorModelParameters>
type PriorTrainer = class interface ITrainerEstimator<BinaryPredictionTransformer<PriorModelParameters>, PriorModelParameters> interface IEstimator<BinaryPredictionTransformer<PriorModelParameters>>
Public NotInheritable Class PriorTrainer Implements IEstimator(Of BinaryPredictionTransformer(Of PriorModelParameters)), ITrainerEstimator(Of BinaryPredictionTransformer(Of PriorModelParameters), PriorModelParameters)
To create this trainer, use Prior
Input and Output Columns
This trainer outputs the following columns:
|Output Column Name||Column Type||Description|
||Single||The unbounded score that was calculated by the model.|
||Boolean||The predicted label, based on the sign of the score. A negative score maps to
||Single||The probability calculated by calibrating the score of having true as the label. Probability value is in range [0, 1].|
|Machine learning task||Binary classification|
|Is normalization required?||No|
|Is caching required?||No|
|Required NuGet in addition to Microsoft.ML||None|
|Exportable to ONNX||Yes|
Training Algorithm Details
Learns the prior distribution for 0/1 class labels and outputs that.
Check the See Also section for links to usage examples.
Auxiliary information about the trainer in terms of its capabilities and requirements.
Trains and returns a BinaryPredictionTransformer<TModel>.
Returns the SchemaShape of the schema which will be produced by the transformer. Used for schema propagation and verification in a pipeline.
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.
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.