# NaiveBayesMulticlassTrainer Class

## Definition

The IEstimator<TTransformer> for training a multiclass Naive Bayes model that supports binary feature values.

public sealed class NaiveBayesMulticlassTrainer : Microsoft.ML.Trainers.TrainerEstimatorBase<Microsoft.ML.Data.MulticlassPredictionTransformer<Microsoft.ML.Trainers.NaiveBayesMulticlassModelParameters>,Microsoft.ML.Trainers.NaiveBayesMulticlassModelParameters>
type NaiveBayesMulticlassTrainer = class
inherit TrainerEstimatorBase<MulticlassPredictionTransformer<NaiveBayesMulticlassModelParameters>, NaiveBayesMulticlassModelParameters>
Public NotInheritable Class NaiveBayesMulticlassTrainer
Inherits TrainerEstimatorBase(Of MulticlassPredictionTransformer(Of NaiveBayesMulticlassModelParameters), NaiveBayesMulticlassModelParameters)
Inheritance
NaiveBayesMulticlassTrainer

## Remarks

To create this trainer, use NaiveBayes.

### 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

Is normalization required? Yes
Is caching required? No
Required NuGet in addition to Microsoft.ML None
Exportable to ONNX Yes

### Training Algorithm Details

Naive Bayes is a probabilistic classifier that can be used for multiclass problems. Using Bayes' theorem, the conditional probability for a sample belonging to a class can be calculated based on the sample count for each feature combination groups. However, Naive Bayes Classifier is feasible only if the number of features and the values each feature can take is relatively small. It assumes independence among the presence of features in a class even though they may be dependent on each other. This multi-class trainer accepts "binary" feature values of type float: feature values that are greater than zero are treated as true and feature values that are less or equal to 0 are treated as false.

## Fields

 The feature column that the trainer expects. (Inherited from TrainerEstimatorBase) The label column that the trainer expects. Can be null, which indicates that label is not used for training. (Inherited from TrainerEstimatorBase) The weight column that the trainer expects. Can be null, which indicates that weight is not used for training. (Inherited from TrainerEstimatorBase)

## Properties

 Auxiliary information about the trainer in terms of its capabilities and requirements.

## Methods

 Trains and returns a ITransformer. (Inherited from TrainerEstimatorBase) (Inherited from TrainerEstimatorBase)

## Extension Methods

 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 are often formed into pipelines with many objects, so we may need to build a chain of estimators via EstimatorChain 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.