> 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/feature-types.md). # Feature Types A model needs to know whether a column is a label, an ordered rank, or a real number, because encoding and regression treat those differently. This page separates variable types, then discrete features, then continuous ones, and how categorical predictors become indicators. The same notes are in [Regression](/predictive-ml/regression.md). The original Feature Types handout, a no permission doc, used to sit here; it no longer opens and is kept at the end of the page. ## Variable types Before a column can be called discrete or continuous, it needs a measurement level. The [Variable types:](http://www.socialresearchmethods.net/kb/measlevl.php) note on levels of measurement is the reference for the three used here: nominal (weather), ordinal (order var 1,2,3), and interval (range). ## Discrete The measurement levels split first into discrete features, which come as numbers or as categories. Categorical data are variables that contain label values rather than numeric values, and the number of possible values is often limited to a fixed set. Categorical variables are often called [nominal](https://en.wikipedia.org/wiki/Nominal_category), which is the Wikipedia entry on the nominal category. Those labels are usually discrete values such as gender, country of origin, marital status, or high-school graduate. ## Continuous Continuous is the opposite of discrete: real-number values, measured on a continuous scale, such as height and weight. The two meet at the regression step. In order to compute a regression, categorical predictors must be re-expressed as numeric: some form of indicator variables (0/1) with a separate indicator for each level of the factor. The boundary also runs the other way, since discrete features with many values are often treated as continuous, i.e. zone numbers -> binary. ## Deprecated links {% hint style="warning" %} These links and images no longer work. The original wording is kept here. A same-resource copy, when one was checked, is used above. {% endhint %} * Feature Types - no permission doc. This address no longer opens: --- # 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/feature-types.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.