# Using MLJ with Gaussian Process Regression

**URL:** <https://discourse.julialang.org/t/using-mlj-with-gaussian-process-regression/43883>\
**Category:** Machine Learning\
**Created:** [July 29, 2020, 2:00pm UTC](https://discourse.julialang.org/t/using-mlj-with-gaussian-process-regression/43883 "2020-07-29T14:00:01Z")\
**Posts on this page:** 1\
**Page:** 1

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**Author:** ![gideonsimpson](https://sea2.discourse-cdn.com/julialang/user_avatar/discourse.julialang.org/gideonsimpson/32/1928_2.png) [@gideonsimpson](https://discourse.julialang.org/u/gideonsimpson)\
**Post date:** [July 29, 2020, 2:00pm UTC](https://discourse.julialang.org/t/using-mlj-with-gaussian-process-regression/43883/1 "2020-07-29T14:00:01Z")

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I have a data set on which I would like to perform binary classification, and I would like to see how well Gaussian Process Regression (GPR) does with it. Since I’ve already been experimenting with `MLJ`, I figured I might as well learn to run GPR inside of it, but I’m having a bit of trouble getting started. The key issues that I need to understand are:

1. How should I format my data for `MLJ` + GPR? Each point in my data set is composed of O(1000) complex numbers. Can I work directly with the complex data type, or should I split it into real/imaginary parts? If I use a `DataFrame`, should each column be one of the O(1000) measurements, or can the type be an `Array`?
2. How do I get started with GPR in `MLJ`? I know that this can be accessed via `scikit-learn` (and I have that binding installed), but it would be good to have an example.

Any suggestions are appreciated.