Hi Krish, Nice Video and well explained. Although I would like to point out on the FNR which is FN/P=FN/(FN+TP). Confusion matrix is always confusing. Love your work and I have started following you.
Amazing Explanation giving by Krish Naik Sir. Before that i had watched a lot of explanations on confustion matrix and still it was confusing to me. After watching this video not it can be forgettable. Thank you soo much sir. For all your efforts...
You wrote TP FP TN FN inside the Confusion Matrix cells in the case of binary classification. But, how are we going to right such notation in case of multi-class classification ?
Krish fpr and fnr are explained in wrong way so plzz crt them many of the subscribers following u so plzzz crt them.... And we love the way u teach us... Tq krish..
Hello @Krish, in this lecture you wrote wrong formula for FPR and FNR. FPR = FP / N = FP / (FP + TN); FNR = FN /(FN + TP). But you did correct in your Data Science Interview Question Playlist. Please let me know if I'm right.
Nice explanation Krish, always love your videos. Just one correction, the formula for FPR, FNR is incorrect on the slide. Just check once and update. Thank you
hi there, i m working with election predictions. i have developed the model using 2018 election data-set and test it on 2013 and 2008 election data-set. now my question is that how to get the mean of all confusion matrix for three elections in one single model.
If 900 results are Correct means it can be True Positive or True Negative. Similarly if 100 results are wrong, it can False Positive or False Negative. Please correct me if my understanding is wrong.
Krish u explain in an awesome way but Formula for FPR and FNR is wrong ! Formula for FPR is (FP/N) i.e. FPR=FP/(TN+FP) and formula for FNR is (FN/P) i.e. FNR = FN/(TP+FN)
at 3:19, please correct the confusion matrix.The TN should be 1 instead of 0.
This is the best video on Confusion matrix explanation
Nice explanation :) Following your videos regularly. Thanks for the detailed explanation
I think your channel deserves atleast 100k subscribers by now.
Thank you so much for the very nice explanation, after all, videos here MY DOUBT GOT CLEARED. Once again thank you, Sir.
Sir your all video lectures are more informative and it relly very helpfull thankyou so much sir🙏🙏🙏🙏
Thank you so much for the simplest explanation. Awesome
Clearly explained the confusion matrix. Thank you
Awesome, Your passion is amazing
Awesome Explanation Sir
Hi Krish,
Nice Video and well explained. Although I would like to point out on the FNR which is FN/P=FN/(FN+TP).
Confusion matrix is always confusing.
Love your work and I have started following you.
Awesome explanation
best explanation on confusion matrix
Amazing Explanation giving by Krish Naik Sir. Before that i had watched a lot of explanations on confustion matrix and still it was confusing to me. After watching this video not it can be forgettable.
Thank you soo much sir. For all your efforts...
very good explanation
Great Video :D
Thank you Krish
Nicely explained
Thanks Krish
Awesome , Got it very clear , thank you
nice explanation sir
Thank you sir for clear explanation.
brother u are a legend
great tutorial , please keep uploading more on topics like this , thank you so much
Excellent Video. Thank you for making this so easy.
Thanks Sir you are giving us a great learning stuff
Great lecture ever
at 19:25 FPR = 1 , because FPR = FP/(FP+TN) and FPR + TNR =1
THANK YOU SIR
Very nice explanation bro.Subscribed! Keep doing same kind of videos :)
nice video
Thank you🙏
FPR is FP/N. Not FP/P
Nice video, at 2:53 shouldn't we put value 1 instead of 0 when we get 0 for both actual and predicted value y and y-hat?
Yeah, same doubt.
I think that correction is needed.
you could use That electronic pad for writing, it would be nice overall learned something important.
You wrote TP FP TN FN inside the Confusion Matrix cells in the case of binary classification. But, how are we going to right such notation in case of multi-class classification ?
ua-cam.com/video/HBi-P5j0Kec/v-deo.html
For multi class too it has been discuss. Krish has taken 5 multi class example
TPR = TP/(TP+FN),
TNR = TN/(FP+TN)
FPR = FP/(FP+TN) //correction needed
FNR = FN/(TP+FN) // correction needed
super sir
i like the explaination, but any intuition behind the rates.
Awesome
Sir please make video on roc curve🙏🙏
Krish fpr and fnr are explained in wrong way so plzz crt them many of the subscribers following u so plzzz crt them.... And we love the way u teach us... Tq krish..
Hello @Krish, in this lecture you wrote wrong formula for FPR and FNR. FPR = FP / N = FP / (FP + TN); FNR = FN /(FN + TP). But you did correct in your Data Science Interview Question Playlist. Please let me know if I'm right.
you are right!
2:54 sir, i thought TN is count 1
your correct
Nice explanation Krish, always love your videos. Just one correction, the formula for FPR, FNR is incorrect on the slide. Just check once and update. Thank you
Tell the formula bro.. he didnt give any reply
Now it looks clear for me
Hi i think in the confusion matrix the (0,0) should also be 1 right but you have denoted it as 0
Hi Krish- in this session, Actual '0' and predicted '0' should be '1' and not '0'. Correct me if I am wrong
hi your link is invalid. how may i find a one? thanks
sir good explanation, can we categorize fake reviews and genuine reviews using confusion matrix?
So, If the data set is imbalanced then how to make it balanced? or how can we predict the correct accuracy from this type of data sets?
Hello sir, please create video on ROC curves.
u mentioned FNR=FN/(FP+TN) . Is it correct , or we should say FNR=FN/(FN+TP)
Yes it is confusing me too
Sir,How we apply confusion matrix on large datasets
Hi Krish, I assume all the video is in sequence ..
How to reduce the false positive and false negative to make our model better one
SIR FOR Confusion Matrix of multi-class are there any concepts of TP, FP, TN, FN.
sir false positive n false negative is it correct???
hi there,
i m working with election predictions. i have developed the model using 2018 election data-set and test it on 2013 and 2008 election data-set. now my question is that how to get the mean of all confusion matrix for three elections in one single model.
If 900 results are Correct means it can be True Positive or True Negative. Similarly if 100 results are wrong, it can False Positive or False Negative. Please correct me if my understanding is wrong.
At 2:54 ,in forth block u wrote 0 but I think it might be 1...
Am I right or wrong please reply?
Should have been 1. He missed it, anyways even this scenario can be accepted.
Krish u explain in an awesome way but Formula for FPR and FNR is wrong ! Formula for FPR is (FP/N) i.e. FPR=FP/(TN+FP) and formula for FNR is (FN/P) i.e. FNR = FN/(TP+FN)
I think you need to switch FP and FN !
no. he is right. check the predicted and actual labels.
FPR=FP/(FP+TN)
FNR=FN/(FN+TP)
How does 0,0 become 0 not 1
19:50 tpr calculation was wrong
Hey George,
Yes just made a small mistake.Sorry for that.
@@krishnaik06 what should be the correct value?
Wrong formula. How confidently he is teaching wrong formula!
change your thumbnail . thumbnail showing multiclass and you r discussing 2x2
great tutorial , please keep uploading more on topics like this , thank you so much