# Evaluation with Core

**URL:** <https://forum.rasa.com/t/evaluation-with-core/3514>\
**Category:** Rasa Open Source\
**Created:** [December 13, 2018, 8:54am UTC](https://forum.rasa.com/t/evaluation-with-core/3514 "2018-12-13T08:54:16Z")\
**Posts on this page:** 2\
**Page:** 1

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**Author:** ![datistiquo](https://dub1.discourse-cdn.com/flex013/user_avatar/forum.rasa.com/datistiquo/32/124_2.png) [@datistiquo](https://forum.rasa.com/u/datistiquo)\
**Post date:** [December 13, 2018, 8:54am UTC](https://forum.rasa.com/t/evaluation-with-core/3514/1 "2018-12-13T08:54:16Z")

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Hi,

Can you do hyperparamter optimization with Core’s evaluation?

It would be nice to have the impact of a let’s say a categorical slot on next prediction in a story.

Sometimes just including a slot value doesn’t change anything in prediction. So it would be great to have an evaluation to analyse what are the features for predicting next actions.

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**Author:** ![amn41](https://dub1.discourse-cdn.com/flex013/user_avatar/forum.rasa.com/amn41/32/20_2.png) [@amn41](https://forum.rasa.com/u/amn41)\
**Post date:** [December 20, 2018, 5:48pm UTC](https://forum.rasa.com/t/evaluation-with-core/3514/2 "2018-12-20T17:48:33Z")

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there are a couple of different questions here.

you can use a library like hyperopt to do hyperparameter optimization. You’ll have to write a small script that wraps Rasa Core’s `train` and `evaluate` methods though.

understanding the impact of a single feature (say a categorical slot) on a prediction is a different topic. You could try something like LIME, although I know there are some other libraries for inspecting network sensitivities I don’t have experience with any of them