> 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/decision-intelligence/incremental-learning.md).
# Incremental Learning
A model trained once goes stale as new input keeps arriving; incremental learning extends it instead. The page gives the definition and two diagrams, then streaming examples in Weka, and ends with the Hoeffding tree.
The same notes are in [Follow the regularized leader](/decision-intelligence/follow-the-regularized-leader.md), [Online Learning](/problem-framing/online-learning.md), and [Training Strategies](/evals/training-strategies.md). The same notes are in [Decision Trees](/predictive-ml/decision-trees.md).
The definition comes from the wiki: in computer science, incremental learning is a method of machine learning in which input data is continuously used to extend the existing model's knowledge i.e. to further train the model. The two diagrams below draw that loop; both were copied from the original hosted image.
Incremental learning.
Incremental learning.
The natural place to use it is a stream of time series. Using incremental learning for time series, i.e., [sentiment from twitter api stream, learning from smiley in weka.](https://www.youtube.com/watch?v=jScSxkuSei8\&list=PLm4W7_iX_v4Msh-7lDOpSFWHRYU_6H5Kx\&index=12) is the first example from that playlist. And [time series in weka](https://www.youtube.com/watch?v=9R0mz_gfhBs\&list=PLm4W7_iX_v4Msh-7lDOpSFWHRYU_6H5Kx\&index=3) is the companion video, with some ideas about dealing with time series data, especially date and time.
### Hoeffding tree
The streaming examples still need a model to extend, and the one this page names is the Hoeffding tree. The same notes are in [Decision Trees](/predictive-ml/decision-trees.md).
The note on it is short: it IS STATE OF THE ART.
---
# 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/decision-intelligence/incremental-learning.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.
