Feature/crossvalidation

[antschum]

Implementation of cross validation (which will be used to calculate the overall AUC)
Updated load function to transfer categorical data into dummy variables (so that sklearn random forest classifier and logistic regression classifier can fit the data)
Fixed small errors.

[github-actions]

Hi @antschum,

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Thanks again for your contribution!

[Zethson]

In general I'm not the biggest fan of the term "scorer" because we just set "scores" aka a "score". Might just be me though :)

But besides that just a few minor comments again.

[antschum]

In general I'm not the biggest fan of the term "scorer" because we just set "scores" aka a "score". Might just be me though :)

But besides that just a few minor comments again.

Is scoring better? So because I did have to take the scores and turn them into scorings/scorers so that they take estimators and y values instead of only comparing predicted and true values.

[Zethson]

In general I'm not the biggest fan of the term "scorer" because we just set "scores" aka a "score". Might just be me though :)
But besides that just a few minor comments again.

Is scoring better? So because I did have to take the scores and turn them into scorings/scorers so that they take estimators and y values instead of only comparing predicted and true values.

I see. Yeah no worries, you can keep it :)

[Zethson]

Minor again. Thanks!

[Zethson]

Looks good to me :)