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 known-sized 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 i-th element has the largest value, the predicted label index would be i. Note that i is zero-based index. |
PredictedLabel |
key type | The predicted label's index. If its value is i, the actual label would be the i-th category in the key-valued 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