> For the complete documentation index, see [llms.txt](https://www.mlcompendium.com/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://www.mlcompendium.com/data/datasets-intro.md). # Datasets A model is only as defined as the rows it is trained on. After this chapter the reader can name feature types, find a dataset, and judge whether the split and the label balance are fit to use. The first question about a row is what each column is, and [Feature Types](/data/datasets-intro/feature-types.md) separates a label from an ordered rank from a real number, because encoding and regression treat those differently. Knowing the types is useless if nobody can find the data, so [Data Catalogs](/data/datasets-intro/data-catalogs.md) is the shelf that names what exists and the tools that implement one. A found dataset can still look good in training while its labels are noisy or its easy examples dominate, and [Dataset Confidence](/data/datasets-intro/dataset-confidence.md) uses dataset cartography to decide how much to trust it. With trust settled, [Datasets](/data/datasets-intro/datasets.md) is the work of typing the data, splitting it, and deciding when the sample is fair, from structured versus unstructured data through imbalance and transfer learning. --- # 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 by asking a question. Perform an HTTP GET request on the following URL with the `ask` and `goal` query parameters: ``` GET https://www.mlcompendium.com/data/datasets-intro.md?ask=&goal= ``` `ask` is the immediate question: it should be specific, self-contained, and written in natural language. `goal` is what the user is ultimately trying to achieve, the reason they need the answer. Sharing it helps GitBook give you a better, more relevant answer. A goal is most helpful when it describes the outcome the user wants rather than restating the question. For example, with `ask=how do I create an API token`, a goal like `automate deployments from our CI pipeline` lets GitBook tailor the answer to that use case. 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.