Why Do We Need to Perform Feature Scaling?
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- Опубликовано: 16 сен 2024
- Hello All,
In this video we will be understanding why do we need to perform Feature Scaling. Happy Learning!!
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Thanx bro for that comment :D
thanku bro i got today's motivation
Yup bro.
It’s too late
@@masthanjinostra2981 its never too late bro !!
May God reward you in centuple for your work sir. We can see you are really know what you are teaching, and that you are passionate about teaching it. Thank you.
This is a very clear explanation of what really scaling is. Thanks for uploading this video.
Making video might be of 3 reason
1. You want to learn
2. You want other to learn
3 both 1 and 2
If you want 1 then keep continue
Else please make a systematic video or make a website like where any one can learn you can also make it paid if you want but in minimum cost like 2999 or 3999 rs.
This would be very helpful for you and others..
Its just an advice.
Thank you.
Sure I am planning to start some online courses with a minimum amount
This is amazing content man! Decided to learn ML during lockdown and your videos have been great in explaining the technical aspects when it comes to data.
Keep it up!
Nice!! Simple and clear explanation
This was helpful. Easy and quick answer to the question beautifully explained.
thanks alot for this now i know when to use scaling and when not to use scaling
Gracias por compartir todos tus conocimientos con la comunidad! Eres un crack!
You had mentioned that Feature scaling helps speed up algorithms which use gradient descent. Even though Xgboost is an ensemble learning algorithm, it uses gradient descent to calculate the loss of each model before passing on the wrongly calculated data points into the next model. Isn't it advised to use feature scaling for xgboost then?
Wow, very succinct and simple explanation. Thank you.
I wait for your videos every day... You explain so well. Keep up the good work! :)
your classes are really helpful in understanding the basics of ML.
Wow, you are a amazing instructor of Machine learning. I am so lucky to find your videos. Your explanation is clear and easy to understand. I can tell you really understand and have experience as well. Thank You!
That make sense! Thanks. It's like using "log" when a dataset has a very high standard deviation
thanks brother!! found this very helpful
Grateful for all the knowledge you have shared. It makes my learning journey much more interesting and easier to remember the key concepts now. 🙏🙏🙏😊
Amaizing vid brother just impressed.... Nice way to explain..
Started watching your channel from last week, Such a great videos.
Thanks for highlighting SGD here, never thought in that way. Great Intuition.
amazing explanation, thank u bro
Nice explaination
very good bro.. amazing
This kind of content😍
explained way better than so many paid courses
I got something new is your every video that is very important for my project .thx
Hello sir,
All the videos related to ML posted by you are really helpful.
Your teaching methodology is also very nice and I can learn easily.
Can you make a video related to Audio Processing/ Speaker Diarization?
As I want to dive deep in Speaker separation from the audio recording. It might be useful to others also.
Thank you.
thank you..understood well.
Sir mere liye toh aap hi Andrew NG sir ho thanks sir for this all content
Thank You Sir , The Scaling makes so much sense now
Krish Naik sir! You were amaze with this approach of teaching. A lot better than online courses. And sir, pls arrange videos of playlist in sequence.
Sir, please make video on github, how to use it, how it is help to make profile stronger.
Excellent video!
Thank you so much ❤
thank very muhn blud really aprewcite it fromi srael !
Krish - Great job and thank you for posting, better than my MBA professor presented, I now understand.
what does linear regression have to do with gradient descent? I think the purpose of standardization in linear regression is cuz to reduce the effect of extreme values/outliers have on the model
Parameter optimization for linear regression is done by gradient descent
Nicely explained. :) Thanks K. NAIK. :)
Thank you for clear explanation
Love the way u speak :D
Great! I want to say I am enjoying your each and every tutorial not only learning.
Should be we perform feature scaling in SVM, Naive Bayes, logistic regression and stochastic gradient descent??
If yes, then which method should be preferred
Thank You...
pleade do make a video on how to select features only required for the dependent variable . (when there are hell lot of variables/features ) :)
Something is known as forward selection strategies. You can apply this idea
finished watching
voice of reason!
thank you for the video, it has enlightened me
Nice jacket sir
hello sir, perfect explaination. please can you make vodeo , how to perform CNN on matlab using image dataset..... i will be greatfull to u for this.
Good explanation man thanks
you talked less about why scaling is important, i didnt get it, practical examples would have been better
i think in his stats video on snd he explained
Well explained
Helpful explanation. Thanks.
awesome sir....!!!!
great video! thank you !
Thanks for the video :)
Sir please can you given some practical video on this on real data sets
if there are classification problems, we should do encoding first and feature scale over it ?
Thank you 👌
So basically perform scaling wherever gradient descent or euclidian distance is used?
thx so much
Is there a scenario where feature scaling may adversely affect the algorithm?
No
Think about salary in two different currency. Does feature scaling in this case??
@@souravbiswas6892 is there a proof?
@@appliedskill absolutely
@@dimitrisproios1860 build the model without applying feature scaling, you will get the proof 😄
super sir
7:32 sir, what about naive bayes
It is classification algorithm (Idea came from Baye's theorem). It works very well in text or spam classification problem. For more information you can read others blogs on internet
Bro, is features engineering linked to the machine learning???
Yes it is part of data preprocessing
thats how you earn a subscriber
Do we need to scale our dependent variable Y, as well...
Do we need feature scaling for dataset containing values only 1,0, -1 only
Can you specify from which of your playlist is this video is from
Thank you so much sir Krish! You explained it very well thank you! because of you I clearly understand it now THANK YOU!!
Doesn't XGBoost use Gradient descent to find the the minimum of loss function? Shouldn't we use scaling in that case?
Is scaling necessary for LinearRegression (OLS)?
How about Naive Bayes, it does not deal with any distance but the probability. If my understanding is correct NB doesn't need Feature scaling?
Yes, You are correct.
Hello sir can you organize your statistics and feature engineering playlist. I think it is not in sequence.
Why does scaling not skew the data? I understand why it makes the algorithm more efficient, but I feel like scaling skews the results. Why does it not seem to be the case?
Sir who have an upper edge in data science a cs btech or stats undergrad student
B Tech, even if its Mechanical :)
I am seeing this video and I want the material but google form is closed so if possible can you share the material please
we can use feature scaling on SVR ? it is recommended or not.
can i use feature scaling in Polynomial regression?
Sir can we use java for AI and ML (like weka and deeplearning4j frameworks)
Yes u can definitely
So sir why not companies using java over python because it us faster than python , highly portable , better concurrency etc
@@siddhantagarwal431 Python can do lot..more than just few machine learning libraries..it can do preprocessing, visualization and you almost have libraries to work on anything.. it's very.powerful and writing code is easy!
@@siddhantagarwal431 @Siddhant agarwal i think all there previous work were done in other programming languages such as java and all. But when python entered, they may feel difficult to convert them to python.
I'm still not sure why it is bad to have a big range of numbers, scaling for the normal number or if feature scaled between 0 and 1, will end up giving the same curve shape if the axis scales are adjusted, can someone help me undertsand
I have trained my model on international data based on Decision tree algorithm but when it comes to test set that is based upon pakistani data . So the answers are not coming out to be same because the data set on which the model was trained with the features was not having mentioned any units and in my test data , I have the units as well. So how feature scaling is not important for this particular case i.e. linked with Decision tree algorithm ? do reply
i still have doubt that how does scaling helps because even after scaling the relative distance doesnt change
Computing gets faster
@@shankar3109 ok thanx mate
Did anyone get the feature engineering material that Krish keeps on mentioning? If yes, can you share it?
Hi krish, I am unable fill the google form, it is no longer available. Can you please help me
Sir I didn't get what actually fit and transform do.
Sir their is no video for feature scaling
sir iam ur new subscriber can u please share the material with me
can someone please share the materials
My fav netflix series
Hi
your intent is good but i would say try to explain in such a way that a layman would understand what you are trying to tell...like you did not explain 'why' a lot you just told good things about feature scaling but you did not put time in explaining why is it used..
What about logistic regression?
Yes. Logistics regression also requires feature scaling...
@@shubhamchoudhary5461 Okay thank you! Great video btw!
@@lakshmisri8978 logistic regression can be trained without scaling .
Sir can language like c++ or java can get us jobs
Yes they can...
Hiii bro..
Im passout this year in entc stream.
I don't have knowledg of programming.
Im going to learn java directly... Can i go direct or learn first c or c++??
Please answer 🙏
Then im going to machine learning.
Go direct.. go for python
It dint come out clearly
kuch bhi samajh nhi aaya sirji, aap to kuch or topics pr hi bol rhe ho,,,, aap simple ye btaao ki scaling kyon karni chahiye
Its very confusing video and doesn't answer why do we need feature scaling.
This is what happens when you learn from practical implementation. You have no real mathematical answers to anything. More like somebody learn to press buttons and was able to make some sense of the process. Stop calling Eucledian distance as Eucledian distance unless you can explain people why euclediam norm (L2 norm) is preferred over L1 norm which gives us the Manhattan Distance....
U better check my eucledian and Manhattan distance video ;) you will get to know about the norms.Probably you are taking a conclusion befor seeing all the videos. :-)