# 2.3. Evaluating ML model quality \[CODE PRACTICE]

{% embed url="<https://www.youtube.com/watch?v=QWLw_lJ29k0&list=PL9omX6impEuOpTezeRF-M04BW3VfnPBRF&index=9>" %}

**Video 3**. [Evaluating ML model quality \[CODE PRACTICE\]](https://www.youtube.com/watch?v=QWLw_lJ29k0\&list=PL9omX6impEuOpTezeRF-M04BW3VfnPBRF\&index=9), by Emeli Dral

In this video, we walk you through the code example of ML model quality evaluation using Python and the open-source [Evidently](https://github.com/evidentlyai/evidently) library.

**Want to go straight to code?** Here is the [example notebook](https://github.com/evidentlyai/ml_observability_course/blob/main/module2/ml_model_quality.ipynb) to follow along.

**Outline**:\
[00:00](https://www.youtube.com/watch?v=QWLw_lJ29k0\&list=PL9omX6impEuOpTezeRF-M04BW3VfnPBRF\&index=9\&t=0s) Create a working environment and import libraries\
[02:45](https://www.youtube.com/watch?v=QWLw_lJ29k0\&list=PL9omX6impEuOpTezeRF-M04BW3VfnPBRF\&index=9\&t=165s) Prepare datasets for classification and regression models\
[08:25](https://www.youtube.com/watch?v=QWLw_lJ29k0\&list=PL9omX6impEuOpTezeRF-M04BW3VfnPBRF\&index=9\&t=505s) Build and customize classification quality report\
[14:50](https://www.youtube.com/watch?v=QWLw_lJ29k0\&list=PL9omX6impEuOpTezeRF-M04BW3VfnPBRF\&index=9\&t=890s) Save and share the report\
[16:05](https://www.youtube.com/watch?v=QWLw_lJ29k0\&list=PL9omX6impEuOpTezeRF-M04BW3VfnPBRF\&index=9\&t=965s) Display the report in JSON format and as a Python dictionary\
[18:15](https://www.youtube.com/watch?v=QWLw_lJ29k0\&list=PL9omX6impEuOpTezeRF-M04BW3VfnPBRF\&index=9\&t=1095s) Build and customize regression quality report

That’s it! We built an ML model quality report for classification and regression problems and learned how to display it in HTML and JSON formats and as a Python dictionary.


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