Performance Metrics, Accuracy,Precision,Recall And F-Beta Score Explained In Hindi|Machine Learning 

Sir, I am so happy for the students who don't have a good financial condition or because of English, won't be able to learn Data Science. This channel brings new hope for them, You are an inspiration for us.

First time I could able to understand actual use of metrics after learning for 3 years 😂..nice

for the first time, I got feel in Machine learning. THANK YOU SO MUCH.

1. Start with Recall: Focus on maximizing recall to ensure you capture as many potential crashes as possible. The primary goal is to ensure that as many actual crashes as possible are detected. Missing a crash (high FN) could lead to significant financial losses . By maximizing recall, you reduce the risk of overlooking a critical downturn. This helps in avoiding missed opportunities. 2. Optimize Precision: Once you’ve achieved a reasonable recall, work on improving precision to reduce the number of false positives. This ensures that when your model predicts a crash, it is more likely to be accurate, thus reducing unnecessary panic or overreaction in the market.

this is the only video that gives detailed and simple explanation in 23 min.

Its very good to know that you also answer immediately along with the questions which is a good way of teaching technique.

great teacher ever'

This helped to cover the evaluation metrics quickly in less time, definitely a nice video to see before interview. Thanks the teaching in simple manner.

Great explanation sir, as well as great examples. I was just looking for your videos in order to understand this concept. Couldn't find this topic in English so came here.

Explained so wonderfully, made me understand fully..

I love the way you teach but everything is in bits and pieces . If there was a single playlist for data science with video numbers would have been great to follow .

Best way to make dumb people like me understand the performance measurement of ML models. I was always confused between Recall and Precision. Kudos to you Krish!!

superb explanation.

Excellent, got a very good understanding of all the terms with proper examples

There is a mistake in F Beta score formula

Sir, please make an end-to-end Machine Learning project till deployment in Hindi. It will be very helpful for us,

thankyou so much krish sir for making our concepts crystal clear...again thankyou ...doing hardwork for us

Bhai great video thankyou for the contribution ..

Thanks sir , first time I got clean on this topic

Crystal clear 👍

Sir you are great, Love from Pakistan

Best explanation one can expect!!! Excellent.

Excellent, best wishes ever, Thanks

Your videos are always helpful sir 🙌🏼

absolute clear sir.

But if they asked why I gave more importance to FP or FN....why did I gave them equally importantance ...then what will be the answer

Well done

Thank you so much

sir apne beta value kaise decide ki idhar 1 ya 0.5 ... i mean why for FP it is 0.5

Thank you sir ♥️

06:00 Sir You forget to cut this 😄

Amazing sir thanks a lot

Make it for multiclass classification

Sir arent all these metrics then meant just for logistic regression, if we use LR or smth in which we have multiplie options confision matrix wont work ?

Very amazing

Sir make more videos and keet it up

Bhai In precision is not the TP from all the Actual value(y) or is it from predicted value(y^) ?

Sir if i join your full stack data science course , will you teach in the same way as in this video?? I think you have taught very well !!

sir, provide pdf file for this video lecture.

theory toh samajh aa gaya, practical ke liye kaha se refer kare? Koi paid video hai kya??

so bhai what is a proper example of a balance data set, is there any method/algorithm to balance these data set ? Also if we get unbalanced dataset does it mean the accuracy is low

Amazing tutorial I wish I had watched it before my exams 🫡

Hi krish i m fresher in data science and i want to know how will i get the job?

recall

👍

18:13 sir is case me to ager model sabhi ko cancer bata de to bhi ye best model rahega aapke logic ke hisab se q ki as u said person at lest test to karwa lega :P this question ask in interview I'm not able to answer.

F 1 score and f beta score same he kya

where r this lecture notes

Tomorrow Stock market is going to crash that scenario i use recall bcz when (actually stock market are crush but model says it not crush so i use) plz sir corrrect or not reply me?

Hello krish sir can u tell me which drawing app or software you are using ?

Bhai what is support in the F beta score ?

Imbalance dataset miss ho gya video me lagging k karan

Hello sir. We use precision when FP is important. Then what is the need of F beta score like we use beta=0.5 when FP>FN. could you please explain it.

iska answer

pERFECT

actually in confusion matrix you mentioned wrong FP and FN just swap it then it is correct sir [1,0] = FN and [0,1] is FP

Is has a small correction which is rows represent actual class and columns represent prediction class

Sir what if Precision score and Recall score both become 0? Thanks

Hello sir Sir apna video ko ku bda diye timing08:34 pe