SdcaMaximumEntropyMulticlassTrainer Class
Definition
Important
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The IEstimator<TTransformer> to predict a target using a maximum entropy multiclass classifier. The trained model MaximumEntropyModelParameters produces probabilities of classes.
public sealed class SdcaMaximumEntropyMulticlassTrainer : Microsoft.ML.Trainers.SdcaMulticlassTrainerBase<Microsoft.ML.Trainers.MaximumEntropyModelParameters>
type SdcaMaximumEntropyMulticlassTrainer = class
inherit SdcaMulticlassTrainerBase<MaximumEntropyModelParameters>
Public NotInheritable Class SdcaMaximumEntropyMulticlassTrainer
Inherits SdcaMulticlassTrainerBase(Of MaximumEntropyModelParameters)
 Inheritance

SdcaTrainerBase<SdcaMulticlassTrainerBase<TModel>.MulticlassOptions,MulticlassPredictionTransformer<TModel>,TModel>SdcaMaximumEntropyMulticlassTrainer
Remarks
To create this trainer, use SdcaMaximumEntropy or SdcaMaximumEntropy(Options).
Input and Output Columns
The input label column data must be key type and the feature column must be a knownsized vector of Single.
This trainer outputs the following columns:
Output Column Name  Column Type  Description 

Score 
Vector of Single  The scores of all classes. Higher value means higher probability to fall into the associated class. If the ith element has the largest value, the predicted label index would be i. Note that i is zerobased index. 
PredictedLabel 
key type  The predicted label's index. If its value is i, the actual label would be the ith category in the keyvalued input label type. 
Trainer Characteristics
Machine learning task  Multiclass classification 
Is normalization required?  Yes 
Is caching required?  No 
Required NuGet in addition to Microsoft.ML  None 
Exportable to ONNX  Yes 
Scoring Function
This trains a linear model to solve multiclass classification problems. Assume that the number of classes is $m$ and number of features is $n$. It assigns the $c$th class a coefficient vector $\textbf{w}_c \in {\mathbb R}^n$ and a bias $b_c \in {\mathbb R}$, for $c=1,\dots,m$. Given a feature vector $\textbf{x} \in {\mathbb R}^n$, the $c$th class's score would be $\tilde{P}(c  \textbf{x}) = \frac{ e^{\hat{y}^c} }{ \sum_{c' = 1}^m e^{\hat{y}^{c'}} }$, where $\hat{y}^c = \textbf{w}_c^T \textbf{x} + b_c$. Note that $\tilde{P}(c  \textbf{x})$ is the probability of observing class $c$ when the feature vector is $\textbf{x}$.
Training Algorithm Details
See the documentation of SdcaMulticlassTrainerBase.
Check the See Also section for links to usage examples.
Fields
FeatureColumn 
The feature column that the trainer expects. (Inherited from TrainerEstimatorBase<TTransformer,TModel>) 
LabelColumn 
The label column that the trainer expects. Can be 
WeightColumn 
The weight column that the trainer expects. Can be 
Properties
Info  (Inherited from StochasticTrainerBase<TTransformer,TModel>) 
Methods
Fit(IDataView) 
Trains and returns a ITransformer. (Inherited from TrainerEstimatorBase<TTransformer,TModel>) 
GetOutputSchema(SchemaShape)  (Inherited from TrainerEstimatorBase<TTransformer,TModel>) 
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. 
Applies to
See also
 SdcaMaximumEntropy(MulticlassClassificationCatalog+MulticlassClassificationTrainers, SdcaMaximumEntropyMulticlassTrainer+Options)
 SdcaMaximumEntropy(MulticlassClassificationCatalog+MulticlassClassificationTrainers, String, String, String, Nullable<Single>, Nullable<Single>, Nullable<Int32>)
 SdcaMaximumEntropyMulticlassTrainer.Options