-Nice. It is helpful to run in Jupyter Notebook. Thank you

From the Feature Importance graph, Delicassen has the highest F score. Doesn't this mean that Delicassen was the most important feature as opposed to Grocery which was fourth best?

No answer to malambomutila comment?

Excellent case study!

I was only able to get an accuracy of about 93% with xgboost.

Thanks a lot

I like how you described each parameter meaning I had no idea you could use Drop out using D.A.R.T

From the Feature Importance graph, Delicassen has the highest F score. Doesn't this mean that Delicassen was the most important feature as opposed to Grocery which was fourth best?

yes u are right @malambomutila

nice work

your code is goat

Small correction: under Command line parameters I think reg:logistic is meant for classification problems with probabilities and binary:logistic is for classification problems with only decision, not the other way around. Great notebook, cheers.