Hi! I have been building a tool that integrates statistical engines - specially scikit-learn - with django called django-ai <https://github.com/math-a3k/django-ai/tree/covid-ht>.
With that tool, I have built another, covid-ht <https://github.com/math-a3k/covid-ht>, which should showcase the power of those together. That tool is meant to help health professionals with classification tasks based on measurements <https://covid-ht.readthedocs.io/en/latest/beyond_covid19.html>. The tool is heading to its first release as a technology preview, and in this process I have faced a release-blocker issue for which I would like to ask for your help: I can't find a consistent interpretation of the graphs. The graphs are called "conditional decision functions <https://covid-ht.readthedocs.io/en/latest/classification/graphing.html>", where each one is the contour of the decision function of a classifier for an observation in 2 variables while leaving the others fixed. The graphs show classification regions as expected, but my initial interpretation seems wrong (commented out <https://raw.githubusercontent.com/math-a3k/covid-ht/master/docs/classification/graphing.rst> ). If that explanation was good, I would expect that perturbing one variable in a direction where the graph shows another class should switch the classification, as the remaining variables are fixed and that should be the value that the classifier uses to decide - which is plotted in that plane. That is not happening, as you may check here <http://covid-ht.herokuapp.com/> (the classifier being used is an Histogram-based Gradient Boosting Classification Tree). Any insight about the situation will be highly appreciated and thankful in advance.
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