# Sklearn adapter for HTM?

**URL:** <https://discourse.numenta.org/t/sklearn-adapter-for-htm/11756>\
**Category:** Implementations\
**Created:** [February 18, 2025, 12:32am UTC](https://discourse.numenta.org/t/sklearn-adapter-for-htm/11756 "2025-02-18T00:32:19Z")\
**Posts on this page:** 2\
**Page:** 1

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**Author:** ![jordan.kay](https://yyz2.discourse-cdn.com/flex030/user_avatar/discourse.numenta.org/jordan.kay/32/458_2.png) [@jordan.kay](https://discourse.numenta.org/u/jordan.kay)\
**Post date:** [February 18, 2025, 12:32am UTC](https://discourse.numenta.org/t/sklearn-adapter-for-htm/11756/1 "2025-02-18T00:32:19Z")

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I’d love to use sci-kit-learn or sklearn or even just a python package to use the HTM to produce future predictions. What’s the best implementation to use?

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**Author:** ![cezar\_t](https://yyz2.discourse-cdn.com/flex030/user_avatar/discourse.numenta.org/cezar_t/32/4451_2.png) [@cezar\_t](https://discourse.numenta.org/u/cezar_t)\
**Post date:** [February 20, 2025, 8:56pm UTC](https://discourse.numenta.org/t/sklearn-adapter-for-htm/11756/2 "2025-02-20T20:56:34Z")

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I think one big issue is that HTM addresses “intelligence” as a problem of continuous on-line learning and prediction of a data stream.

frame1 → next\_frame\_guess → frame 2 → (corrective learning +) next\_frame\_guess → frame 3 …

While sklearn has a quite different (statistical?) metaphor. Input data set and a corresponding output data set for training, and another pair of input-output data set for testing.

Closer to HTM would be RNN (LSTM, GRU) models (without the online learning capability), or even (remotely related) reinforcement learning but I don’t think any of these are represented in sklearn.