> For the complete documentation index, see [llms.txt](https://docs.raga.ai/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://docs.raga.ai/ragaai-prism/test-inventory/tabular-data.md). # Tabular Data RagaAI supports a wide range of Structured Data applications, including Binary Classification, Multi - Class Classification, Regression, Timeseries Forecasting and Ranking. #### Binary and Multi - Class Classification Binary Classification is a foundational task in machine learning that divides occurrences into two separate classes. The primary purpose is to assess whether or not an observation falls into a specific category, making it useful for applications such as spam detection in emails, fraud detection in financial transactions, and medical diagnosis for binary outcomes such as disease presence or absence. Multi-Class Classification, on the other hand, broadens the idea to cover cases in which instances are allocated to numerous exclusive classes. This technique is critical for applications such as sentiment analysis where the goal is to correctly assign a given data point to one of several predetermined categories. Its adaptability makes it useful in a wide range of applications, including natural language processing and recommendation systems, and quality control in manufacturing processes. RagaAI ensures that these models can perform well in diverse scenarios. *** --- # Agent Instructions This documentation is published with GitBook. GitBook is the documentation platform designed so that both humans and AI agents can read, navigate, and reason over technical content effectively. Learn more at gitbook.com. ## Querying This Documentation If you need additional information that is not directly available in this page, you can query the documentation dynamically by asking a question. Perform an HTTP GET request on the current page URL with the `ask` query parameter, and the optional `goal` query parameter: ``` GET https://docs.raga.ai/ragaai-prism/test-inventory/tabular-data.md?ask=&goal= ``` `ask` is the immediate question: it should be specific, self-contained, and written in natural language. `goal` is optional and describes the broader end goal you are ultimately trying to accomplish on behalf of the user. GitBook uses it to tailor the answer towards what is most useful for that goal. The response will contain a direct answer to the question and relevant excerpts and sources from the documentation. Use this mechanism when the answer is not explicitly present in the current page, you need clarification or additional context, or you want to retrieve related documentation sections.