I don't know how to put in words but a big big THANK YOU Krish for making such amazing videos. These are the kind of explanations that I have been searching over for months.
Fantastic explanation. The best thing about this video is you explained where is real (IT) world we can use, which matrix to use when. probably one of your masterpieces.
Amazing explanation Krish. Definitely I will watch this video again, before when I'm attending interviews. So many things will be clarified in 20 min. of explanation.
At first I was really confused with where things were going because, a lot of it works well for me if examples are shared to explain the scenario (which were later included - Spam and Cancerous/Non-Cancerous). I have been referring to a lot of notes from many sources and none of them could give me the kind of clarity this video gave me on what metrics to focus on and when. Thanks a ton for sharing this.
ANY 10TH STANDARAD STUDENT HERE?THANKS FOR THIS VIDEO I'M JUST BLESSED, THERE IS NO CHANNEL DOING THIS FOR OUR SYLLABUS THANKS A LOT MAN DIDN'T EXPECT SUCH A GREAT TUTORIAL HERE ON YT
If youtube provides 10000+ subscribe and like buttons , I would have like and subscribed all of them.. THank you very sir for bringing such an amazing content for free.
I'm waiting for part 3 of "implementing all the Metrics For Classification Problem in ML by taking improper data set" as you mentioned in the above video here 1:05 please Krish. As I'm preparing for the interviews as a fresher and I'm immensely in need of the job now and I have a year of a gap also, So I'm sitting continuously to quench my thirst for covering ML part as quickly as possible. so please Krish make the video.
Krish Sir, you're really a super hero compared to Hrithik Roshan's Krrish. :D Everytime I watch your video, my desire to meet you in person is increasing day by day. Wonderful video as always. You made the concept super clear. Would love to learn more from you. Thank you.
Sir, I am a graduate research student and currently working on my thesis. This video was very helpful. I wanted to learn about ROC,AUC and kappa. I have to defend in 2 months. When could we expect part2 and part3 video, Sir? Could you please let me know.
Hi Krish, In the video @16:16 seems there is small correction FP and FN should be interchanged if i am not wrong.. as 1-0 is is FN and 0-1 is FP.... correct me if i am wrong.
1 is for True and 0 is for false and also consider 1 as positive and 0 as negative, so [1,0] is TrueNeg, [0,1] is FalsePos, [1,1] TruePos and [0,0] is FalseNeg
In a pathological database there are 2000 patient records out of which 600 are flu patient records. A disease predictor application accesses that database and predicts 150 flu patients correctly out of 200 flu patients and 300 non-flu patients. Find the following performance parameters. a) Sensitivity b) Specificity c) Accuracy d) Precision
that is a very nice tutorial ever to easily understand these parameters. But sir Did you interchange the fn and fp place on your confusion matrix table. just on minute 13:00. because even in your other tutorials and other video lectures they put fn on the right corner and fp on the left bottom. thank you if I am mistaken.
Hi krish, Very nice explanation. Just only one question. In your point of view, model performance is more important or model accuracy. from my point of view it is model accuracy because for model performance we can create pickle file and can maintain the model performance. Please correct me if i am wrong.
Your explanation between 14:45 to 14:55 will confuse the understanding of precision. It says "Out of the total actual positive predicted results" where as it should have been "Out of total positive predicted results"
I have almost completed my masters in data science and they only mentioned F1-Score, never F-beta score. You are a very good teacher!!!
Because they follow old syllabus
F1 -score where Bets=1 is for equal weighting of precision and recall. Beta will change depending if your problem should favor precision (or recall).
Because this is Krish that is Oldish
Where did you pursue your masters?
this field is so vast that one institute cant tech each and everything, you have to continously gain knowledge from wherever you find
I don't know how to put in words but a big big THANK YOU Krish for making such amazing videos. These are the kind of explanations that I have been searching over for months.
he is a hero, not the hero we deserved but the hero we needed
This is the best explanation of Metrices i have ever gone through...Fantastic Job!!
I was confused after my lecture in college, just knew names of method but now got to know when to apply what. Thank you Krish, you are Awesome Teacher
I only started learning AI / ML few weeks back and this explanation was brilliant and you made it so easy to understand the concept.
Fantastic explanation. The best thing about this video is you explained where is real (IT) world we can use, which matrix to use when. probably one of your masterpieces.
Hello sir!I cannot imagine how you are helping me at this time of my final defense.God bless you sir .salute you sir thx from the neighbor.
Best explanation. In the university, I just learned the formulas but never understood them. Very clear explanation!
Sir bhagwaan apko sari khusiya de...splendid job for sharing such valuable concepts
Amazing explanation Krish. Definitely I will watch this video again, before when I'm attending interviews. So many things will be clarified in 20 min. of explanation.
Thanks for your consistency with your learning and teaching.
thanks a lot, man!! I really needed this as hell in my one of the ml projects.. and here u uploaded this video..
He is saving me in my masters and clearing my concepts.Thank You sooo muchhh Sir.GOD Bless You:)
I finished watching the entire playlist Thank you a lot
superb! many congratulations!
@@drishtisharma6843 where is the part 2?
@@abhinavkale4632 check out the complete machine learning playlist. Watch video 41 for next part.
What is the playlist name?
At first I was really confused with where things were going because, a lot of it works well for me if examples are shared to explain the scenario (which were later included - Spam and Cancerous/Non-Cancerous). I have been referring to a lot of notes from many sources and none of them could give me the kind of clarity this video gave me on what metrics to focus on and when. Thanks a ton for sharing this.
Wonderful explanation very sorted & clear , I never got clear understanding on performance matrix before this. Great job i must say..!!
This is the best channel i came across so far.
ANY 10TH STANDARAD STUDENT HERE?THANKS FOR THIS VIDEO I'M JUST BLESSED, THERE IS NO CHANNEL DOING THIS FOR OUR SYLLABUS THANKS A LOT MAN DIDN'T EXPECT SUCH A GREAT TUTORIAL HERE ON YT
Incredible explanation, I just started my evaluation phase in my churn prediction model and this will be really helpful, thank you!
Krish always explains and makes concept simpler and make us remember for eternity
you are a savior bcz.... out college faculties are hopelesss..
This is one of your master class video as of now....Thanks Kris...
Thank you Krish Sir,for explaining precision and recall so effectively, previously it was very confusing.
This is the best explanation for performance metric selection in classification problems. Thank you Krish!!
If youtube provides 10000+ subscribe and like buttons , I would have like and subscribed all of them.. THank you very sir for bringing such an amazing content for free.
Actually All these topics are very tough to understand but krish explained it very well.. Thanks a lot Krish
Thank you so much for deep driving the each concept related to the Data Science/Machine learning 😊😊
I'm waiting for part 3 of "implementing all the Metrics For Classification Problem in ML by taking improper data set" as you mentioned in the above video here 1:05 please Krish. As I'm preparing for the interviews as a fresher and I'm immensely in need of the job now and I have a year of a gap also, So I'm sitting continuously to quench my thirst for covering ML part as quickly as possible. so please Krish make the video.
Wonderful explanation. Loved your teaching style. Thanks for this amazing content !
Thank you a lot, this is really very simple and clear. This is the best explanation I have ever seen, you are amazing.
Most excellent video I have ever watched on this topic. Thanks a lot krish, it helped me a lot!!!
Great explanation Krish .But we need to remember the things to apply them.Thanks
Krish Sir, you're really a super hero compared to Hrithik Roshan's Krrish. :D Everytime I watch your video, my desire to meet you in person is increasing day by day. Wonderful video as always. You made the concept super clear. Would love to learn more from you. Thank you.
Loved the explanation. Thank you for sharing this info with such simple explanations
superb, awesome and great video sir...
you are a great teacher and a person too..
I love your videos sir👌👌👍❤❤❤🙏🙏🙏🙏🙏🙏🙏🙏😊😊😘😘😍😍🥰
I really enjoy your lecture, Krish. Thanks a lot for putting this video.
You're an amazing teacher! Thanks for this video
Brilliant comprehensive playlist 👍🏻👍🏻
Thanks buddy. Now I have full clarity about recall, precision and f beta.
this is nugget! Krish explained so well like crystal clear. thanks!
Most Awaited video!!! Thank you soo much sir..... 👍👍👍
Literally the best explanation
too good kris. very useful and informative video
Great Sir, I was badly in search to clear all my concepts, I really learned a lot. Thanks Sir
best video on youtube.. Thank you
Amazing explanation. Thank you so much :) You are always the best!
Thank you so much for teaching this good. I really understood the topics . You are a great teacher. 💖💖
Thank you Krish for Wonderful explanation
Best explanation..Hats off Krish
Hey, krish. It is a great video; I could learn a lot and clear few aughts I had with performance metrics. Thank you .. ! Good luck...
kudos to you. Very well explained.
Sir, I am a graduate research student and currently working on my thesis. This video was very helpful. I wanted to learn about ROC,AUC and kappa. I have to defend in 2 months. When could we expect part2 and part3 video, Sir? Could you please let me know.
Thank you sir for explaining further the concepts
thanks Sir for so much efforts to teach so as to make our concepts clear
Superb Kris.. Egarly waiting for next part
this is one brilliant video! thank you very much.
Thank you sir..for the efforts you have taken to explain!!
Great explanation Krish! Thank you!
Superb explaination.. sir
Excellent video Krish. Kudos!
oh my god no words to comment explained very well thanks a lot........
Hi Krish,
In the video @16:16 seems there is small correction FP and FN should be interchanged if i am not wrong.. as 1-0 is is FN and 0-1 is FP.... correct me if i am wrong.
1 is for True and 0 is for false and also consider 1 as positive and 0 as negative, so [1,0] is TrueNeg, [0,1] is FalsePos, [1,1] TruePos and [0,0] is FalseNeg
Excellent with clear explanation 👌👏
Good classification sir about performance metrics for novice learner
very very nice video sir, Great Explanation..thanks a lot
Excellent explanation. Thank you
In a pathological database there are 2000 patient records out of which 600 are flu patient records. A disease predictor application accesses that database and predicts 150 flu patients correctly out of 200 flu patients and 300 non-flu patients. Find the following performance parameters.
a) Sensitivity
b) Specificity
c) Accuracy
d) Precision
Very helpful video. Thanks Krish :)
Absolutely understandable! Thanks a lot!
Great video! But, how do we mathematically/ practically find out if we have a balanced/ unbalanced dataset?
Kindly upload 1 vs 1 classification problem as well. I really like your videos. It is very well explained
thanks for your explanation, I took notes !
Amazing,Very Intresting
Thanks a lot krish, waiting for part-2&3 videos.
Excellent Summary Krish 👍
A great video. Thanks a lot.
Thanks a lot krish, waiting for part-2&3 videos , i am really thank full for your videos, great work man
Already uploaded just follow the complete ML playlist
@@krishnaik06 can't find please share link
7:55 Is the basic calculation of all these metrics (FPR, FNR, Recall, Precision, F beta, ROC AUC) confusion matrix?
very good video krish...
that is a very nice tutorial ever to easily understand these parameters. But sir Did you interchange the fn and fp place on your confusion matrix table. just on minute 13:00. because even in your other tutorials and other video lectures they put fn on the right corner and fp on the left bottom. thank you if I am mistaken.
Krish you are amazing!!!!
Thank a ton for your valuable inputs
Very informative video. Thanks for this.
Thank you Krish for this nice video
Hi krish, Very nice explanation. Just only one question. In your point of view, model performance is more important or model accuracy. from my point of view it is model accuracy because for model performance we can create pickle file and can maintain the model performance. Please correct me if i am wrong.
Great Krish.👍
Hats off Krish!
Thanks you so much, for the great insight!
You my friend are godsend 👍
mind bogling awesome
Thank you sir great tutorial!!
Wonderful Explanation.
Well explained.👏
Excellent sir.
Excellent Explaination👍
Your explanation between 14:45 to 14:55 will confuse the understanding of precision. It says "Out of the total actual positive predicted results" where as it should have been "Out of total positive predicted results"
선생님 감사합니다... 적게 일하고 많이 버세요...
Thank you SO MUCH!!!!!!
it was an amazing explanation
19:28 is F beta the F-1 Score / Harmonic mean?