# Training classification models
<a name="training-classifier-model"></a>

To train a model for custom classification, you define the categories and provide example documents to train the custom model. You train the model in either multi-class or multi-label mode. Multi-class mode associates a single class with each document. Multi-label mode associates one or more classes with each document.

Custom classification supports two types of classifier models: plain-text models and native document models. A plain-text model classifies documents based on their text content. A native document model also classifies documents based on text content. A native document model can also use additional signals, such as from the layout of the document. You train a native document model with native documents for the model to learn the layout information. 

Plain-text models have the following characteristics: 
+ You train the model using UTF-8 encoded text documents. 
+ You can train the model using documents in one of following languages: English, Spanish, German, Italian, French, or Portuguese. 
+ The training documents for a given classifier must all use the same language. 
+ Training documents are plain text, so there are no additional charges for text extraction. 

Native document models have the following characteristics: 
+ You train the model using semi-structured documents, which includes the following document types:
  + Digital and scanned PDF documents.
  + Word documents (DOCX).
  + Images: JPG files, PNG files, and single-page TIFF files.
  + Textract API output JSON files.
+ You train the model using English documents. 
+ If your training documents include scanned document files, you incur additional charges for text extraction. See the [Amazon Comprehend Pricing](https://aws.amazon.com/comprehend/pricing) page for details. 

You can classify any of the supported document types using either type of model. However, for the most accurate results, we recommend using a plain-text model to classify plain-text documents and a native document model to classify semi-structured documents.

**Topics**
+ [Train custom classifiers (console)](create-custom-classifier-console.md)
+ [Train custom classifiers (API)](train-custom-classifier-api.md)
+ [Test the training data](testing-the-model.md)
+ [Classifier training output](train-classifier-output.md)
+ [Custom classifier metrics](cer-doc-class.md)