so for a summary of what I have done:
My dataset has 5 classes and 10 parameters. I used XGBclassifer from sklearn to investigate if I could use those 10 parameters to predict the class of each data point. After training and fitting the XGBclassifier, I checked feature_importances_ and found out that 2/10 parameters played a key role in the classification.
So my question is:
Can I find out exactly how those 2 parameters contribute to the classification of each specific class? For example, can I find the cut-off values for parameter 1 and parameter 2 that will result in the prediction of class 1?
I am thinking of performing unsupervised clustering using those 2 parameters and k value = 5. Afterwards, I can just eyeball the approximate cutoff values. However, I worry that the 5 clusters will not correspond closely to the 5 groups.
Thanks a lot in advanced