@narasimhann2814
Neat explanation in 4mins. Keep making small and informative videos like these :)
@neithmccorkle
Concise explanation, especially helpful in visualizing the differences! Thanks for breaking down these concepts so clearly.
@ansariamaan620
Watching this one day before exams!.. neatly explained
@prathmeshlonkar2387
Crisp visualization & explanation. Loved it!
@coolmanabc1231
Just stumbled across your video, I am looking forward to watching all your videos š
@kimsungho4114
Your video was very helpful for my Datamining class. Thanks!
@zbady4595
Iām learning ML and these videos are fantastic
@mateusfigueiredo9961
Thank you for the clear explanation!
@LauraArdon-v2b
It was very instructive
@sabrinesabrina7255
perfect explanation
@houstonfirefox
Great video. My understanding is that you would almost always use Bagging, evaluate the results and, if good enough, stop there. However, you COULD go on to try various boosting methods to see if the model improved even more but at what cost? If the best boosted model (Adaboost, XGBoost, etc) performed 1% better but took 3x longer to compute then boosting the already-bagged models might not be worth it right? Still trying to cement in my mind the process flow from a developer standpoint š
@jasonwang-wg8wu
this was dope, color-coding kinda threw me off but the overall explanation was nice and concise.
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@PythonArms
great video, loved the pictures. a caveman like myself loves the pictures. thank you lol
@ritshpatidar
It was clear. Thanks
@kimchijam
simple and easy to understand, nice
@RumayzaNorova
Very good explanation :)
@iacobsorina6924
Awsome video! Keep going!
@AnnaRushi
thanks for easy explaination brother
@datamlistic