Very nice and instructive. Thank you!

Although there is something I don't understand. When ranking the features with Rank1D, humidity has the highest Shapiro score and light the lowest. You concluded that humidity is the strongest indicator of the occupancy. But in the end showing the feature importances of the DecisionTreeClassifier, we see that it is the other way around. Any explanation comes to mind?

I am a machine learning engineer at Armis and I love to learn and share my passion for data science — https://www.linkedin.com/in/adrien-biarnes-81975717

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