@Hoxle-87
Great video series. Don’t stop making them. Maybe take another app/tool/methodology and break it into parts like you did with SHAP. Very digestible.
@saremish
I really enjoyed such a deep discussion about the clear distinction between correlation and causation!
@shrishchandrapandey801
Amazing work, Conor! Keep them coming. These 6 mins have helped clarify so many topics!
@yijunfu6808
best youtuber explaining SHAP I have found!
@zahrab3390
WOW! Such an amazing explanation on SHAP! I really enjoyed. Thank you.
@jenilsaliya3769
good explanation on topic , thank you sir
@k44zackie
Many thanks from Japan!
@yashnarang3014
Great video!!!
@cesarepiscopo2549
AMAZING WORK!
@azizjedidi1180
Great video man. Thank you very much.
@AZ-ph7gg
Great explanation!
@sasaglamocak2846
Great video. You mentioned that KernelSHAP suffers from extrapolation if features are correlated, like other permutation based methods. What about TreeSHAP with e.g., XGBoost?
@NA-ug5eq
Amazing video. Thank you so much. I have one question please: When explaining kernelShap, what do you mean by permuting values, please? What does mean grey circles in the graph at time 2.28, please? Does permuting refer to changing features order ( this is not clear in the graph in video at 2.28) or it refers to replacing some feature values with random values? Thank in advance for your response
@TheTomdwy
i signed up for the newletter but can't get the free course
@tschess7
I am confused. You said that Machine Leaning only cares about correlations not association but should it be said "only cares about correlations not causation"?
@escolhifazeremcasa
Is there any way to deal with limitation 2: Feature Dependencies ?
@mdabubakarchowdhurysunny2846
can show some code about LIME
@adataodyssey