For which of the following document types is the ML Classifier specifically designed?

The ML Classifier is specifically designed to classify highly unstructured documents. These documents typically do not follow a consistent format or structure, making it challenging to extract information using traditional methods. The ML Classifier utilizes machine learning algorithms to learn patterns from the data, allowing it to categorize documents based on their content rather than relying on predefined templates or fields.

Highly unstructured documents may include various formats such as emails, customer feedback, social media posts, and more. Because they lack clear organization, the ML Classifier's sophisticated capabilities enable organizations to extract insights and make data-driven decisions from such documents effectively.

In contrast, structured documents, which have a fixed layout and fields, are better suited for traditional data processing methods. Semi-structured documents, while having some organizational elements, still don’t require the complex learning that highly unstructured documents do. Therefore, the ML Classifier is particularly tailored for the challenges posed by highly unstructured documents, positioning it as the best fit for this option.