Polynomial Regression | Machine Learning

[@MohitSingh-jb9tb]

If anyone wants to learn ML then it is the best channel.. explanation is amazing for all the topics.

[@deeksha-cm8kq]

Sir You are really one of the best teachers on youtube , the way you teach and specially they way you create examples shows how much you know about the stuff you are teaching. Thankyou so much Sir. I wish one day I have this amount of knowledge so that I can also make a difference like you .

[@ankitchoudhary9302]

Sad to Say but It feels Like some People just copied Your youtube content , put it in a course and made hell lot of Money and Launched their Companies. They might have stolen my Money but you Earned My respect !!!!

[@stardusthere]

which course you r talking about bro?

[@ug1880]

Tell which one....we will expose him

[@ankitchoudhary9302]

@@ug1880I won't name it but you know cheapest things often attract most buyers.

[@sanjaymange6904]

great video bro , excellent , perfect , no words to express my gratitude. U covered all the doubts I had w.r.t to polynomial regression

[@0Fallen0]

6:03, PolynomialFeatures class for x1 and x2 and for degree 3, also adds columns (x1)(x2), (x1^2)(x2), (x1)(x2^2). Generally, for PolynomialFeatures set to degree = d, and we originally had n features, we will get a total of (n+d)! / d! n! features. Just thought this'd be useful to know.

[@ruchiagrawal6432]

thanks! I had the same doubt because at 5:52 sir didn't add the additional term x1*x2, so I got confused. Please correct me if I'm wrong.

[@thatsfantastic313]

​@@ruchiagrawal6432 it gets features’ high-order and interaction terms. suppose (x1,x1) becomes (1,x1,x2,x1^2,x1x2,x2^2) and so on

[@subhajitdey4483]

from where/how the term (X1)(X2) coming from ? Kindly elaborate

[@manishsinghmehra7555]

​@@subhajitdey4483 formula bro (a+b)^n where n is the degree of polynomial.

[@rb4754]

You way of teaching is very very interesting.

[@messi0510]

1:35-->7:25 What is polynomial Linear Regression?

[@laxmimansukhani1825]

great explanation !! every student owes you alot !!

[@raj-nq8ke]

Perfect explanation as always.

[@saptarshisanyal6738]

Your explanation is wonderful. I request you to kindly prepare a video to explain the code.

[@balrajprajesh6473]

Best of the best!

[@alihasanmerchant9335]

Best, simple & easy explanation

[@hari_thaniwal]

Watch this video with the subtitles provided by KZbin. You will find another story in the subtitles 😅

[@SonuK7895]

Thanks for the Video Sirjiiii

[@colabwork1910]

Awesome Explanation.

[@zainfaisal3153]

Sir you generated data in form X and y, what about, if we have real life dataset, then how to plot data distribution?

[@amitbaderia6385]

Excellent. Very well explained. You should use real world data instead of random numerical values

[@JACKSPARROW-ch7jl]

Thank u nitish 🎉

[@1981Praveer]

we might have to use "print("Input", poly.n_features_in_)" instead of "print("Input", poly.n_input_features_)"

[@sandipansarkar9211]

finished watching

[@heetbhatt4511]

Thank you sir

[@rafibasha4145]

Hi Nitish bro,this audio is not clear ..please do new video if possible on same topic..also please cover fearure selection xgboost

[@balrajprajesh6473]

you can use earphones, it will be understandable.

[@devendrasharma5567]

Infinit time thankyou ir

[@Sreedeviha]

I understood the explanation well.. thanks... but Bhai one doubt in real data set by looking at the data without plotting graph, will I be able to tell we need to use poly reg.. Also if we add features like X^2,X^3, will it not end up with multicollinearity as dependency b/w input features exists now??

[@thatsfantastic313]

Yes it adds multi-collinearity in your model by introducing x^2, x^3 features into your model. To overcome that, you can use orthogonal polynomial regression which introduces polynomials that are orthogonal to each other.

[@subhajitdey4483]

One qurstion.... 10:37 why we are not using fit_transform() for X_test_trans as X_train_trans ??

[@manishsinghmehra7555]

fit() is used to calculate mean and variance of the data, while transform() is use to transform (scale the data according to our need) the data. When we first use fit_transform() which already calculated mean and variance from 80% of data, which is more reliable than the 20% of data i.e. X_test_trans also by doing so we keep the mean and variance of whole dataset same that's why we only use transform for X_train_trans.

[@rohinisingh6916]

Thank you ​@@manishsinghmehra7555

[@Ishant875]

Why in polynomial regression with degree 2 is not able to capture the bowl in the training dataset? I think that curve shown was for a higher polynomial.

[@Tusharchitrakar]

Yes even I was confused because the poly features should have fit better for degree 2

[@sanjeevranjanmishra8811]

Can we apply polynomial regression on dataframe (let's say df) first then after we split x into train_x or test_x. If yes then why everyone doing split first then transform x_train then x_test.

[@manishsinghmehra7555]

Why do we create X_new and y_new, while we have X_test_trans and y_pred

[@randomlyaj4735]

Same doubt

[@krishnakanthmacherla4431]

Wow

[@namanmodi7536]

nice

[@ahmadansari7708]

❤️

[@WhySachi]

❤

[@anuragkothari8384]

You should have write the code from scratch for polynomial feature generation

[@rk_dixit]

plz spend a little more time on code writing or code explanation 🙏

[@khushisaini2683]

Avaj ni saaf