# Using TM as classifier?

**URL:** <https://discourse.numenta.org/t/using-tm-as-classifier/7125>\
**Category:** Machine Learning\
**Created:** [January 30, 2020, 4:38pm UTC](https://discourse.numenta.org/t/using-tm-as-classifier/7125 "2020-01-30T16:38:39Z")\
**Posts on this page:** 1\
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**Author:** ![rhyolight](https://yyz2.discourse-cdn.com/flex030/user_avatar/discourse.numenta.org/rhyolight/32/3922_2.png) [@rhyolight](https://discourse.numenta.org/u/rhyolight)\
**Post date:** [January 30, 2020, 5:59pm UTC](https://discourse.numenta.org/t/using-tm-as-classifier/7125/2 "2020-01-30T17:59:35Z")

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I’m not sure that will work, but I know this can work:

> [@Analysis of different scalar series](https://discourse.numenta.org/t/analysis-of-different-scalar-series/1678/2):
>
> One technique you might use is something I call “classification by anomaly score”. If you have a set of sources that produce data streams, train a model for each source and get anomaly likelihoods out of them over time. Once you feel that they are fully trained and not throwing false positive anomalies, turn learning OFF on all the models. Now you have a suite of models that can classify new data streams. Just pass the new data stream into each model simultaneously. Over time, track the model w…

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