Tutorial 41-Performance Metrics(ROC,AUC Curve) For Classification Problem In Machine Learning Part 2

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  • Опубліковано 19 жов 2024

КОМЕНТАРІ • 186

  • @skviknesh
    @skviknesh 3 роки тому +16

    The explanation is great!
    Why is it used?
    1. ROC & AUC - Plotted between TPR & FPR, helps in - visualization & explanation & selection of a required threshold for the model!
    What is ROC & AUC?
    2. ROC - (Receiver Operating Characteristic curve) that you have drawn & AUC is the - (Area under the curve).
    How does the curve look like?
    3. AUC should be greater than the 0.5 line drawn, which indicates a better model.
    🙇‍♂️🙇‍♂️🙇‍♂️

  • @LIMLIMLIM111
    @LIMLIMLIM111 4 роки тому +102

    This channel is a gold mine. Thank you for your knowledge Krish.

  • @suhaillone831
    @suhaillone831 3 роки тому +12

    This guy is definitely one of the best teacher available on UA-cam
    Simple but effective explaination
    Lots of love for you Sir ♥️♥️♥️♥️♥️♥️♥️♥️♥️♥️♥️♥️♥️♥️♥️♥️♥️♥️♥️♥️♥️♥️♥️♥️♥️♥️♥️

  • @borhanmukto7770
    @borhanmukto7770 Рік тому +3

    Bro, You are a gem. I mean not a single unnecessary word, everything is explained clearly and concisely. Many many thanks brother

  • @hassamdaudi
    @hassamdaudi 2 роки тому +11

    I swear to God man, I learn more from you than my professors at school... you're saving my assignments

    • @DappuDon
      @DappuDon 7 місяців тому

      In which school we are teaching ML?

  • @hilaav7449
    @hilaav7449 3 роки тому +1

    Hi I couldn't find anywhere part 3 - performance metrics for classification problem part 3 ( you said in the first one there are 3 parts I only found 2).By the eay, I became a memeber more than half a year ago to support your work, because you are an excelent lecturer and you helped me a lot.

  • @athanikarammy7476
    @athanikarammy7476 3 роки тому

    great explanation video..anyone confused or overwhelmed about how much to study and from which channel to study...simply follow krish naik playlist from start to end.....

  • @mandeepsinghnegi1931
    @mandeepsinghnegi1931 3 роки тому

    Sometimes, I check this, that and then just search if you have made a video on the topic. No one can just simply explain better. You are an AVENGER.

  • @sandipansarkar9211
    @sandipansarkar9211 4 роки тому +1

    Thanks krish.My doubts about ROC ad AUC are now clear.

  • @syedyunus4204
    @syedyunus4204 4 роки тому +8

    Krish, please make a video on "implementing all the Metrics For Classification Problem in ML by taking improper data set" as you mentioned in one of the videos of ML playlist (#1:05 minute of Tutorial 34 of Complete ML playlist)

  • @KiranKumar-bb8lr
    @KiranKumar-bb8lr 2 роки тому

    really ur communication ur voice and the way u explain it stole my heart broooookeep rocking broooo

  • @lumar_music
    @lumar_music 4 роки тому +37

    Please consider using manual focus and iso :D

  • @smiling_madly596
    @smiling_madly596 3 роки тому +5

    you're a true supervisor for supervised learning! 🌷

  • @ayberkctis
    @ayberkctis 3 роки тому +3

    Hey Krish, you are amazing! You explain topics clearly. So, everybody can understand it!

  • @fancy4926
    @fancy4926 3 роки тому +2

    Krish, at the end of this video, you said: "if they focus on both TPR and FPR, we can also choose TH(0.4) at that plot". But in real ROC there are always a lot of inflection points like TH(0.4), then which inflection point should we choose?

  • @firasfakih3050
    @firasfakih3050 3 роки тому +1

    I just hit the jackpot with this channel, thanks alot

  • @ayushijonwal9209
    @ayushijonwal9209 3 місяці тому

    Very clear and crisp explanation, great job👏

  • @guillermotorres4988
    @guillermotorres4988 3 роки тому

    After reading about roc and auc, your example calculating manually the values was perfect to finally understand this topic. Thank you!

  • @yashodhansatellite1
    @yashodhansatellite1 4 роки тому +2

    Hats off Krish..u r so deep in knowledge

  • @agenthitman7564
    @agenthitman7564 Рік тому

    Finally I have understand ROC....Thank YOu Sir

  • @sahithim9278
    @sahithim9278 4 роки тому +3

    for the threshold value of y^=0.4 and y^=0.6 , tpr and fpr values is same i.e.,(0.5,0.5) and for y^=0.8 is (0,0.5). Now the graph will be different and AUC might be less than 0.5.

  • @souvikmaitra6653
    @souvikmaitra6653 4 роки тому +1

    awsum explaination...ALL THE BEST for all your upcoming videos #Krish.

  • @chaytanyakumar8939
    @chaytanyakumar8939 2 роки тому

    Best explanation on the internet. Thanks Krish!

  • @230489shraddha
    @230489shraddha 2 роки тому

    Krish you explain data science concepts so clearly that I have become fan of your's !!! thanks a ton man!!!

  • @amoldeshmukh8244
    @amoldeshmukh8244 4 роки тому +5

    Hey Krish , Did you uploaded Part-3 of this video (i.e. Performance Metrics for Classification problem) ?

  • @teamannonymous163
    @teamannonymous163 3 роки тому

    totally clear .. you way of explanations are really very amazing.

  • @lavanyathippineni6341
    @lavanyathippineni6341 3 роки тому +1

    Superb explanation👌👏👏👏....Waiting for part-3. Please upload it as possible as sir

  • @rishigupta2342
    @rishigupta2342 2 роки тому

    Brilliant explanation. Please make part 3 of this video.

  • @megalaramu
    @megalaramu 4 роки тому +3

    Hi Krish, Awesome explanation. Thanks a ton. Could you please help us by giving an example (Part-3) for all the performance metric in python.

    • @kanakkumar5261
      @kanakkumar5261 4 роки тому

      Hi Krish, awesome teaching skills... please upload all parameters in python programming language take an example like iris data

  • @sohinimitra7559
    @sohinimitra7559 3 роки тому

    Hi Krish, your videos are very informative and helpful. I saw your video where you explained ROC-AUC based on thresholds in Logistic Regression. What is the intuition about thresholds for other classification models? For example, a decision tree will split based on feature value to determine the class, how is ROC constructed here? Similarly, for other classification algorithms, how is ROC constructed?

  • @chandrusaekarb3445
    @chandrusaekarb3445 4 роки тому +1

    Nice Video and clear explanation. Thanks a ton ! .. Please do post part-3 of the metrics soon....

  • @mohammadarif8057
    @mohammadarif8057 4 роки тому

    Sir people like you should be admired ...big fan hope we meet one day

  • @abhishekranjan2617
    @abhishekranjan2617 3 роки тому +1

    Awesome video sir.
    I love it😍😍🥰🥰😘😊😊🙏🙏🙏🙏🙏👌👌👌👌

  • @pranjan85
    @pranjan85 4 роки тому +4

    Hi Krish, Can you please upload Part-3 so that everything can be understood

  • @sumitnaina
    @sumitnaina 3 роки тому

    You explained it so nicely and make me understand the roc and auc concept so easily. Thanks a lot krish

  • @happytorev466
    @happytorev466 4 роки тому +1

    Thank you for this video. Eagerly waiting for part 3.

  • @fortask5897
    @fortask5897 2 роки тому

    Lovely Explanation.

  • @narenderbansal5741
    @narenderbansal5741 4 роки тому +1

    Very nicely explained. Thanks for sharing.

  • @saikatkar547
    @saikatkar547 2 роки тому

    Very nicely presented ...clearly understood . Thank you sir

  • @xyzaex
    @xyzaex 4 роки тому

    Brilliant. You are officially Sensei.

  • @adiflorense1477
    @adiflorense1477 3 роки тому +1

    8:20 here I finally understand how to read the ROC curve. You really are great

  • @MrYoonyan
    @MrYoonyan 3 роки тому

    The best explanation ever seen, thank you

  • @tengliyuan1988
    @tengliyuan1988 4 роки тому

    Krish I really appreciate your effort in sharing your knowledge. Thanks

  • @kaisersayed9974
    @kaisersayed9974 4 роки тому +2

    Sir please make videos on web scrapping for beginners.. please sir and I am anabel to get the link of AQI website which you have shown in the video ....but please make more videos on web scrapping..

  • @kranthikumar7635
    @kranthikumar7635 4 роки тому +1

    really awesome explanation....

  • @chubiandchuba4804
    @chubiandchuba4804 4 роки тому +1

    Hi Krish you are really superb... Excellent knowledge, well explained

  • @ravinch7973
    @ravinch7973 2 роки тому

    Explain about epoch and its graphs with accuracy, Precision, Recall, f1Score. Also show in python or matlib how to draw

  • @mr.top5636
    @mr.top5636 2 роки тому

    Your explanation is just amazing go ahead man

  • @avinashmandora8745
    @avinashmandora8745 Рік тому

    What an informative video. Really amazing!

  • @hemrajbhatt1034
    @hemrajbhatt1034 4 роки тому

    Thanks a lot sir. I was seriously waiting since your last video on performance metrics.

  • @dordonduty2918
    @dordonduty2918 3 роки тому

    very short and srisp, i love it

  • @ianmutuma6564
    @ianmutuma6564 4 роки тому

    Thanks Krish. This saved me a couple of hours

  • @agnelraju718
    @agnelraju718 3 роки тому

    superb explanation loved each bit😍😍

  • @shaz-z506
    @shaz-z506 3 роки тому +1

    Good video Krish, could you please create a video on Cohen-Kappa score.

  • @jagadeeshabburi570
    @jagadeeshabburi570 3 роки тому

    Maza agaya bhai dekeke. Subscribed. The thing what I felt lacking was. More explainanation

  • @ankita684
    @ankita684 4 роки тому +2

    Hi...can you share the link for part 3 of the performance metric video.
    Also, can you share any real life example when domain expertise would want threshold to be say 0, 0.6.?

  • @godse54
    @godse54 3 роки тому

    krish please make part 3 and also list some performance matrix for multiclass classification.

  • @preetigupta1349
    @preetigupta1349 2 роки тому

    I think Krish, if you can include Gini index computation part that would add lot of value to this part of series.

  • @myGiG-09
    @myGiG-09 Рік тому

    Awesome lecture

  • @sonalighadage6253
    @sonalighadage6253 3 роки тому

    very nice explanation

  • @ozcanf6680
    @ozcanf6680 3 роки тому

    Great video, thank you Krish

  • @tannurohela6192
    @tannurohela6192 2 роки тому

    Great explanation but I didn't get how to take the threshold values , this is , the values we have taken to predict and draw ROC. Can we select them randomly or is there a way?

  • @subhasrik1436
    @subhasrik1436 Рік тому

    sir excellent ,plz explain eer from det curve

  • @sampsonagudze8674
    @sampsonagudze8674 4 роки тому +1

    Fantastic!

  • @kaustubhshete6250
    @kaustubhshete6250 3 роки тому

    Awesome Work Sir !!

  • @krishnab6444
    @krishnab6444 2 роки тому

    perfectly explained

  • @louerleseigneur4532
    @louerleseigneur4532 3 роки тому

    Thanks Krish

  • @rajeshjha9246
    @rajeshjha9246 4 роки тому +1

    sir, please give one walk though session on classification problem with all Performance Metrics(ROC,AUC Curve,precsion,recall etc.)

  • @binnypatel7061
    @binnypatel7061 4 роки тому

    Keep up the good work.....Great explanation!

  • @pshubhamoy21
    @pshubhamoy21 3 роки тому

    nicely explained

  • @narenderbansal5741
    @narenderbansal5741 4 роки тому +1

    Hi Krish, Suppose i want to set the threshold at .75. then how i can do this in python code. is there any parameter in ruc_curve method or have to do it by using logic/code.

  • @SuperRia33
    @SuperRia33 Рік тому

    Hi Krish
    Please dnt get me wrong but seems fpr(specificity) formula is wrong it's tn/tn+fp wrt oreily statistics book

  • @asawanted
    @asawanted 3 роки тому

    So comparing the Andrew Ng video, the true positive rate is same as recall? and the false positive rate is same as precision?

  • @nirojghimire1706
    @nirojghimire1706 3 роки тому

    Very nicely explain, need to improve video quality.

  • @sanketannadate4407
    @sanketannadate4407 4 місяці тому

    it really helped thnx

  • @Amd107
    @Amd107 2 роки тому

    One construtive feedback: please lock your phone's focus so that it doesn't go haywire like at 1:47

  • @hemantdas9546
    @hemantdas9546 4 роки тому

    Super Sir. Very clear

  • @akashs9414
    @akashs9414 3 роки тому

    Actually, many will sort those probabilities in descending order and use them as thresholds.

  • @adityagandhamal2351
    @adityagandhamal2351 4 роки тому

    Thank you sir....It was very helpful.

  • @hussainlokhandwala2245
    @hussainlokhandwala2245 4 роки тому +1

    Values of Th(0.4)==th(0.6) and for th(0.8) we have values (0,0.5) so also the curve is changing.... Please correct me if I'm wrong :)

    • @stacksmack
      @stacksmack 2 роки тому

      I also got the same value. you are right

  • @mohsin10185
    @mohsin10185 3 роки тому

    Excellent

  • @tuannguyen4802
    @tuannguyen4802 3 роки тому

    Amazing explanation

  • @neelroy4153
    @neelroy4153 2 роки тому

    Hi Krish, how did you come up with original y hat?

  • @sibamarcel9428
    @sibamarcel9428 4 роки тому

    You have great knowleg...Thanks a lot

  • @sushilchauhan2586
    @sushilchauhan2586 4 роки тому +2

    CAN YOU PLS EXPLAIN MULTICOLINEARITY AND HOW TO AVOID IT ? PLS or PERTUBATION TEST

  • @dheerajkumark2268
    @dheerajkumark2268 4 роки тому +1

    Sir can you plz make videos on statsmodal and scipy library

  • @abhishek-shrm
    @abhishek-shrm 4 роки тому

    Great video Sir.

  • @saivenkat8813
    @saivenkat8813 3 роки тому

    hi krish in the previous lecture u said fpr =fp/p =fp/tp+fn now in this video it is fp/fp+tn which one is correct

  • @adiflorense1477
    @adiflorense1477 3 роки тому +1

    0:52 how to determine the threshold. can we set the threshold ourselves?

  • @anujlahoty8022
    @anujlahoty8022 4 роки тому

    Awesome video.

  • @avinashreddy5779
    @avinashreddy5779 2 роки тому

    thanks for your knowledge Krish sir,
    I am new to date science ROC used to evaluate the logistic regression only or do we have any uses can anyone help with this

  • @laxmimansukhani1825
    @laxmimansukhani1825 Місяць тому

    awesome !!

  • @avisii8928
    @avisii8928 7 місяців тому

    hello sir, i am working on my final year project, Resume classification and ranking system using knn and cosine similarity, the roc auc curve for all my class label came 0.5, what should i do?

  • @itwind9912
    @itwind9912 Рік тому

    Can we use AUC-ROC for multiclafication problem?

  • @tomasvonbischoffshau
    @tomasvonbischoffshau 2 роки тому

    Thanks! really.

  • @sonamkori8169
    @sonamkori8169 4 роки тому

    Thank you Sir

  • @shaunakchadha4204
    @shaunakchadha4204 3 роки тому

    Krish sir...... Hrithik Roshan ke bajaae aapko le rahe hain agli Krish Movie mein ??? Hero ho aap sir

  • @aayushijain4024
    @aayushijain4024 4 роки тому

    Sir in your confusion matrix video, you write precision formula as TP/TP+FP and this you use FP/TN+FP, which one is correct???

    • @HhhHhh-et5yk
      @HhhHhh-et5yk 4 роки тому +2

      Here its not precision , we calculate False Positive rate and True positive rate!

  • @RomeshBorawake
    @RomeshBorawake 4 роки тому

    Hi Krish, Thank for the video. For calculation AUC score we have to sort y^prob or threshold list in accending order?

  • @nareshkumar-dc7bq
    @nareshkumar-dc7bq 4 роки тому +4

    HI Krish, I really appreciate you can provide me the link to part 3 session of the performance matrix please.