Evaluating Classifiers: Gains and Lift Charts

[@EstherPhilip-nu4go]

Thanks for this clear presentation. I finally understood it. I appreciated you going back and forth between the slides, showing the connections.

[@JeanLouisKali]

Thank you very much, Dr Sadawi. This video has completely lifted the clouds that lingered in my mind concerning these important charts. Your presentation is one of the clearest I had the chance to come accross. I just had a "Aha" moment! Really, big thanks.

[@DrNoureddinSadawi]

Thanks for the nice comment!

[@anupammisra8982]

Best explanation on the internet.

[@KrishnaSatishReddy]

Great explanation

[@MasterofPlay7]

wow that's a very simple and complete explanation, thank you so much!

[@palloshi1]

this is best and cleanest explanation i have seen

[@vaishalibisht518]

Thank a lot, Sir. This is the only video which has explained this concept so clearly. Kudos to you :)

[@wiambenhammou9942]

Thank you Dr. Sadawi! your videos are clear and very helpful.

[@yeweibin5495]

Thank you for sharing, the explanation is detail and straightforward

[@xiaoyuzhu8652]

The idea of lift curve is very much like that of roc auc

[@jayphansalkar6542]

Thank you for the great explanation!

[@mbayogesh]

Awesome Video.. Now I can say that I know about Gain lift charts..Thank you Dr. Sadawi :)

[@dorsaghahri7126]

Please make more videos, maybe on regression models. This is great.

[@saikatkar547]

Positive responses means prediction = positive and actual = positive . Right???

[@ferryt2544]

Really helpful. Thanks!

[@arpanasingh8957]

Thanks for such clear explaination. Kindly help us in building model and evaluation using weka

[@DrNoureddinSadawi]

Thanks for watching
Can you please provide more details?
Have you seen my tutorial on WEKA API:
ruclips.net/p/PLea0WJq13cnBVfsPVNyRAus2NK-KhCuzJ

[@arpanasingh8957]

No

[@MrSmarthunky]

Hi All, @Noureddin
... Any idea how we got the responses 6000 & 10000 in first and second row @ 5:32 ? Thanks.

[@MasterofPlay7]

just some make up numbers from the predictive model lol

[@MegaSb360]

Thank you so much !!!