This channel has become one of my favorite platforms to learn ml, owing to the crisp explanation by Aman.
@oluwafemiolasupo401823 күн бұрын
Nice one here. Thank you for the simplicity employed in explaining the core concepts.
@cenxuneff2 күн бұрын
Excellent explanation
@abiramimuthu61993 жыл бұрын
Thanks a lot aman. Great video, Teaching is an art and you are doing justice to that every time by breaking down the concept to little steps and explaining it in a way it reaches everyone. keep up your good work.........i am expecting more videos in your NLP playlist
@UnfoldDataScience3 жыл бұрын
Thanks a ton Abirami. Hope you and your family are staying safe and good.
@nikhilpawar78762 жыл бұрын
Looking at the content & no. Of subscribers. Highely underrated
@UnfoldDataScience2 жыл бұрын
Kindly share within your groups Nikhil, that may help. tq
@chdoculus Жыл бұрын
listen to this video 3 times.. lot of insights. Thank you.
@vivekkumaryadav88022 жыл бұрын
YOU ARE TRUE KNOWLEDGE
@UnfoldDataScience2 жыл бұрын
Thanks Vivek, cheers.
@asafjerbi18672 жыл бұрын
Hi, excellent explanation but I have some points which are not clear to me yet. 1. how you choose the criteria to split the XGBoost tree by? for instance, you chose 'age
@santoshvjadhav10 ай бұрын
Gr8 video Sir. You have explained it clearly and in a very simple way. Thanks a lot 🙏
@UnfoldDataScience10 ай бұрын
So nice of you Santosh. Please share with friends.
@animeshbagchi78813 жыл бұрын
one of the best explanation on complex intuition of XG Boost.....
@UnfoldDataScience3 жыл бұрын
Thanks Animesh.
@user-px3ux9we6e7 ай бұрын
Thanks a lot for this excellent video! I am still curious about how xgboost can achieve parallelization and how it handles missing values as you mentioned before. Looking forward to your new videos!
@maruthiprasad81848 ай бұрын
Superb simple explanation, Thank you very much
@nikhilpawar78762 жыл бұрын
Definitely best & most understandable explanation of XGB🔥
@UnfoldDataScience2 жыл бұрын
Cheers Nikhil.
@ruchitagarg48713 жыл бұрын
Very nicely explained, Thanks Sir. One of the best videos I have seen on KZfaq.
@UnfoldDataScience3 жыл бұрын
Thanks Ruchita.
@samarkhan25093 жыл бұрын
very nice video.brief..concise..to the point..agree with others..probably the best explanation so far on youtube .way to go bro
@UnfoldDataScience3 жыл бұрын
Your comments are my motivation Samar. Thanks for motivating.
@Krishna-pm8ty2 жыл бұрын
Very NIce Explanation!
@UnfoldDataScience2 жыл бұрын
Thanks Krishna.
@Krishna-pm8ty2 жыл бұрын
@@UnfoldDataScience Simplyifying concept with out loosing the complexity. One of the best explanation in youtube. Your channel really deserves more visibility. All the very best Aman.
@killeraudiofile80943 жыл бұрын
Thanks a lot for this. Very helpful for me as I am brushing up on ML theory for interviewing. Awesome work!
@UnfoldDataScience3 жыл бұрын
Glad it was helpful!
@sachin295962 жыл бұрын
Sir, In formula new prediction=old prediction + Learning rate * output. I didn't understand how to get the output value as 6 for the second record. Could you explain once again.
@srk57027 ай бұрын
formula = sum of residuals/no. of residuals
@miroslavstimac43843 жыл бұрын
Excellent explanation.
@UnfoldDataScience3 жыл бұрын
Thanks a lot for watching Miroslav.
@sadhnarai87572 жыл бұрын
Very good Aman
@UnfoldDataScience2 жыл бұрын
Thank you.
@subhz13 жыл бұрын
Nice explanation!!!! Can you please make a video on XG Boost, Gradient boost where the dependent variable is binary/categorical in nature say Good/Bad(0,1)
@UnfoldDataScience3 жыл бұрын
Great suggestion Subhadip. Noted.
@ranajaydas89063 жыл бұрын
Sir, can you please tell why you didnt square the SR in12:08. And can you tell how the output at 14:02 is 6? What does the output actually mean?
@avneshdarsh98803 жыл бұрын
output value calculated as the average of residuals in our case (4+8)/2=6
@akashkewar3 жыл бұрын
1) We square the sum of residuals when we compute similarity score and not when we make a prediction. 2) As we are making a prediction, and assuming lambda is 0, prediction is just an average of all the values (residuals) in a particular leaf. 3) output means residuals, we predict the residuals (because it is a boosting algorithm) such that the weighted sum of all the residuals is close to the target variable as much as possible (final prediction by our model).
@utkarshsaboo452 жыл бұрын
@@akashkewar Are you sure we "square the sum" and not "sum the square"? The "square of sum" in the video doesn't make sense!
@avinashajmera2775 Жыл бұрын
Hi aman please clear this
@preranatiwary76904 жыл бұрын
Hey good one again! Continue your good work.. Thanks
@UnfoldDataScience4 жыл бұрын
Thanks for the feedback.
@drsivalavishnumurthy344 жыл бұрын
Good explanation sir.Kindly make a video on SVM and alternate decision tree
@datadriven5972 жыл бұрын
Awesome indepth explanantion, keep up the good work man!
@UnfoldDataScience2 жыл бұрын
Glad you liked it!
@sachink91026 ай бұрын
Q1. how to interpret Similarity Score. Q2. what is meaning of High Similarity Score and Low Similarity Score
@sandipansarkar92112 жыл бұрын
finished watching
@HrisavBhowmick3 жыл бұрын
12:01 why not square of sum of residuals as u said in the formula?
@ahmedidris305 Жыл бұрын
At 10:27 I don't understand why the similarity score after the split is affected by the change of the lambda value before the split "Why the similarity score after the split will go down?". As I understood from the video, the split rule has nothing to do with the lambda value, therefore if lambda value changed, the split remains the same. the only thing changes is the gain, when the lambda value goes up, then the smilarity score before the split decreases and the gain increases because the deducted value (similarity score before the split) decreases when lambda value gets higher.
@JoshDenesly3 жыл бұрын
This the best vedio in XGboost.
@UnfoldDataScience3 жыл бұрын
Thanks Vish.
@pradeeppaladi8513 Жыл бұрын
Thanks a lot for the lecture. Can you please clarify as to what happens in case of a classification problem? I mean what about the residuals in case of a classification problem as there will no residuals in them. How do we interpret these learnings for a classification problem?
@ahteshaikh1193 Жыл бұрын
Thanks for the excellent work!!
@rafibasha4145 Жыл бұрын
Hi Aman,thanks for the video,please explain how lambda controls overfitting
@Madhuram_Qualityoflife4 жыл бұрын
I like the way u explain complex concepts in simple way. Thanks
@UnfoldDataScience4 жыл бұрын
Thanks Vibhaas, ur comments motivate me :)
@Vipulghadi Жыл бұрын
Sir you take only one feature for prediction,what if data have more than 1 feature,on which crieteria ,the model select the feature,Is information gain like approach is use or any other approach...... Please explain sir
@logeshr492327 күн бұрын
can u do one for xgboost classification
@sumitkumardash1193 жыл бұрын
can you just describe the loss function for it ?
@babusivaprakasam98463 жыл бұрын
Straight to the point. Thanks
@UnfoldDataScience3 жыл бұрын
Welcome Babu.
@parvsharma87673 жыл бұрын
Thanks a lot brother..god bless u for ur information
@UnfoldDataScience3 жыл бұрын
Always welcome Parv.
@chdoculus Жыл бұрын
one question- what is the output in the last formula of new prediction. which output it is?
@omarsalam758611 ай бұрын
thank you could you explain how to do feature important using XGboost
@SunilKumar-mz6kr3 жыл бұрын
Great explanation
@UnfoldDataScience3 жыл бұрын
Glad it was helpful Sunil. You're very welcome Goundo. If possible, Please share the link within data science groups. Thanks again.
@sandipansarkar92113 жыл бұрын
great explanation
@UnfoldDataScience3 жыл бұрын
Thanks a lot Sandipan.
@cgqqqq3 жыл бұрын
you are a god...
@UnfoldDataScience3 жыл бұрын
IT'S TOO MUCH :)
@dhineshmathiyalagan64153 жыл бұрын
Very informative. Thanks for explaining the concept such that it is understood easily. I just to want to understand a effect of outlier on the base value(Model 0). Since mean value(which is high in the presence of outlier) is considered initially to calculate the residuals and for prediction, wouldn't it have greater impact ?. Please share your insights.
@UnfoldDataScience3 жыл бұрын
Yes, exactly Dhinesh, there will be outlier impact hence better to take care of it before starting training.
@akshatagrawal6701 Жыл бұрын
Dear Aman ji , One question please ... SS value is SR suqare but when you are calculating for the 11 value you do only sum of residual but not doing their square so please explain how it come for 6 . if do square of SR then value can be different
@UnfoldDataScience Жыл бұрын
I will check - I may have possibly made mistake, did u check previous comments?
@akshatagrawal6701 Жыл бұрын
@@UnfoldDataScience Thanks to Aman Ji for reading your viewers' comments and respecting their doubts. I think in one comment you gave the full paper link and one more link for more detail so I will check from there... thanks
@atomicbreath43603 жыл бұрын
Sir what exactly is difference between base model trees created in gradient boosting and xgboost.? Do gradient boosting also use this above formula which you have shown in the video
@surajprusty69042 жыл бұрын
If we take mean as the criteria then sum of the residual will always be zero if values are taken as it is(with signs)
@ambarkumar78052 жыл бұрын
Is the same procedure for classification?
@debojitmandal8670 Жыл бұрын
Hello sir i dint follow the concept of how it's handling the outlier as you said it handles the outlier but u have not explained how as lamda increases the similarity score decreases but how is it imaoacting or taking care of the outliers i dint follow it as i couldn't understand the relationship bw them Second let's say a new data comes i.e 11 so it goes to the branch greater then 10 and again will a new similarity score be count d bcs u have a 3rd data i.e 11 So (4+8+11)^2/3+0
@jatin78363 жыл бұрын
very explanatory video, great work bro, I just need to ask one thing, that output thing at the end, how we got 6 as the output because we divide (4+8)^2 / 2+0 = 72 ,if we do not square this we get 6, but the formula is with square right?, so how we got that 6 as the output? it must be something else(may be72 i think), please explain.
@UnfoldDataScience3 жыл бұрын
Thanks jatin, will check that. Thanks for pointing out.
@himanshuarora68223 жыл бұрын
Hi Aman, Jatin is correct. It should be 72 instead of just 6. If we take 72, the value of residual is (34-51.6)= -17.6. Please see and suggest if I am correct. Also, is the value of residual is decreasing in this case from 4 to -17.6. How to further reduce it so that it is closer to 0
@r.h.51723 жыл бұрын
@@himanshuarora6822 I have the same doubt. Is this cleared somewhere? Aman, could you pls. explain.
@avneshdarsh98803 жыл бұрын
output value calculated as the average of residuals in our case (4+8)/2=6
@ganeshkharad11 ай бұрын
too good...!!
@UnfoldDataScience11 ай бұрын
Thanks Ganesh
@tempura_edward43303 жыл бұрын
Very clear ! Thank you !✨🙏
@UnfoldDataScience3 жыл бұрын
You’re welcome 😊. Please share my videos in various data science groups you are part of, that will motivate me to create more content :)
@awanishkumar63083 жыл бұрын
Can we apply L1 and K2 regularization technique to any algorithms whether its Linear regression, xgboost, gboost, random forest or etc?
@UnfoldDataScience3 жыл бұрын
Not directly, there are different regularization parameters we can tune in various algo.
@giridharreddy81134 жыл бұрын
Probably the best in youtube. It would be really great if you could make a video of books where you have learnt from and if possible provide book links to amazon.
@UnfoldDataScience3 жыл бұрын
Thanks Giridhar. On books, please find my recommendation below,. you will find links to buy in description of same video: kzfaq.info/get/bejne/oKqnpM2evJeqk5s.html
@jayitabhattacharyya43133 жыл бұрын
From where is output 6 coming? The similarity score for 2nd branch was 72 according to your formula. I fail to understand please help
@harshagarwal81703 жыл бұрын
you can see from previous tree (4+8)/2+0 = 6 here lambda is 0 as said by him ....
@tempura_edward43303 жыл бұрын
I think he means to calculate the new prediction is just: (4+8)/#R, not calculating the similarity score. I got confused too. 😁
@ppsheth914 жыл бұрын
Hello Sir, Really very nice explanation for such a complicated algorithm. Hardly there is any video which describes indepth intuition for Xgboosting.. Thanks a lot Sir.. One doubt : Can u explain how the classification for any new record will take place from test data set? Can you create such videos for Catboost and Light GBM ?
@RameshYadav-fx5vn4 жыл бұрын
Really very nice
@UnfoldDataScience4 жыл бұрын
Thanks Prayag, I will add videos on Catboost and Light GBM as well.
@UnfoldDataScience4 жыл бұрын
Thank you.
@akhilnooney5343 жыл бұрын
Do we calculate IG and Entropy for splitting criteria?
@UnfoldDataScience3 жыл бұрын
No, python does for us.
@AMVSAGOs3 жыл бұрын
Hai Aman, can you please tell us, why the data should be normally distributed. and how does it affects the ML models?
@UnfoldDataScience3 жыл бұрын
Model gets a wider range to learn from. To keep it simple.
@rajeev264u3 жыл бұрын
Thanks Aman for sharing your knowledge. Great learning. Can you please explain the relation between min_child_weight and Gamma. Do we still need to tune min_child_weight if we are using Gamma values for tuning as the tree is getting pruned by using a higher Gamma?
@UnfoldDataScience3 жыл бұрын
Hi Rajeev, about tuning your hyperparameter, you should try with different combinations to see what works good for your model. We can not take a generic approach for all data.
@nishidutta34843 жыл бұрын
Hey Aman, you talked about missing value treatment in XG boost in your previous video..how does XG boost treat missing values?
@UnfoldDataScience3 жыл бұрын
HI NIshi, Sorry for late reply. That will be little long explanation. Please check below link for understanding more: datascience.stackexchange.com/questions/15305/how-does-xgboost-learn-what-are-the-inputs-for-missing-values
@vishnukv65373 жыл бұрын
good explanation :)
@UnfoldDataScience3 жыл бұрын
Glad you liked it Vishnu.
@gauravverma3652 жыл бұрын
Can we generate the mathematical equations between adopted inputs and output parameters after successful implementation of xgboost?
@pacsSaanihaamariyam Жыл бұрын
In the end , the new prediction value is subtracted with iq to find the new residue value when new predictions are done, why is the residue value calculated only for 34 and why not 20 amd 38
@souravbiswas68924 жыл бұрын
Awesome explanation 👍 although it was bit complicated. can you create videos on poisson regression and survival analysis?
@UnfoldDataScience4 жыл бұрын
Thanks Sourav. Yes I will put that in my list.
@shanmukhchandrayama39033 жыл бұрын
Sir, Can you please how this xgboost works for logistic regression.
@UnfoldDataScience2 жыл бұрын
Do u mean classfication?
@shanmukhchandrayama39032 жыл бұрын
@@UnfoldDataScience yeah my bad yes., thanks for taking time and replying sir.
@vikram59702 жыл бұрын
Hi Sir,i couldnot find the link to 'how gradient boost works' the theoretical explanation. i found the one which exlains about why the XGboost is fast and has high performance. can you please give me the link to how XGBoost works.
@JoshDenesly3 жыл бұрын
Please make vedio on "Pipeline" of building model and how it is implement in Production
@UnfoldDataScience3 жыл бұрын
Noted.
@letsplay0711 Жыл бұрын
14:22 , I think output is (12) square, 144/2+0, 72. Please Correct me if wrong...
@UnfoldDataScience Жыл бұрын
Need to check
@drsivalavishnumurthy343 жыл бұрын
Sir nice video sir.pls make a video of the dependent variable is categorical that is yes or no .
@UnfoldDataScience3 жыл бұрын
Ok Vishnu.
@TarashankarSenapati-yz8rv Жыл бұрын
Sir ,how 6 is coming ,u missed to square the sum of 4 and 8, please tell me
@mayanksriv003 жыл бұрын
sir, please do cover light GBM and is advantage over XGboost
@UnfoldDataScience3 жыл бұрын
Thanks Mayank. Noted.
@rafsunahmad48553 жыл бұрын
is knowing the math behind algorithm must or just knowing that how algorithms works is enough? please please please give a reply.
@UnfoldDataScience3 жыл бұрын
Knowing math is must.
@rafsunahmad48553 жыл бұрын
I'm confused because other told me that if I want to do a job which is related to research means improve machine leaning or create new algorithm then I must learn behind the math means how math working behind of an algorithm but for normal data science job it will be enough that how an algorithm work but knowing how math working behind of an algorithm is not must. please give a reply
@lifeisbeautiful11119 ай бұрын
hi can u pls explain how the output is 6 for the second observation in the table?
@ujjwalgoel63597 ай бұрын
yes i was looking for same question cuz the output for right node was 72
@ujjwalgoel63597 ай бұрын
do u know the answer?
@Amit-dl4vd2 жыл бұрын
Where is gradient descent happening in the algorithm?
@rahuljaiswal1414 жыл бұрын
Can you make end to end clustering algorithm. How to select variable, no of clusters and then final deployment
@UnfoldDataScience4 жыл бұрын
Hello, Thanks for feedback, I will note this topic and create video in coming week for sure.
@shivanshjayara63723 жыл бұрын
I dont understood the how the tree will decide which is to be the root node....if it depend on the I.G. then i got it and second thing is.....it will be better if you take more than 3 records in that example.........like 5-6 coz im not able to get whether every row is getting into operation or whole row at once
@dvp16782 жыл бұрын
At 7:19 is it not Age < 10 instead of Age > 10?
@Hu.aventuras2 жыл бұрын
Hi Aman!!!, have a question, how can i predict a gender with mobile data phone with an XGBoost algorithm
@UnfoldDataScience2 жыл бұрын
You need to create data such that your target column is gender and is the u can run xgboost classifier.
@shivanshjayara63723 жыл бұрын
i dont understand formula: firstly u used (sum of res square) & secondly u used only (sum of res) what is reason?
@harivgl3 жыл бұрын
Are all models M1, M2 etc. the same model, data and tree and features used?
@UnfoldDataScience3 жыл бұрын
Depends on what your M1, M2 are, usually same.
@ajaybhatt68204 жыл бұрын
sir please make vedios on RNN ,LSTM
@UnfoldDataScience4 жыл бұрын
Hi Ajay, It will come for sure
@hiteshyerekar22043 жыл бұрын
Hi Aman it's only change one residual i.e 2.2 what about remaining how we get remaining residuals ?
@UnfoldDataScience3 жыл бұрын
In the similar way, I just gave one example Hitesh.
@saurabhdeokar37912 жыл бұрын
In new prediction, which value you take as output?
@UnfoldDataScience2 жыл бұрын
Which part of the video Saurabh?
@saurabhdeokar37912 жыл бұрын
@@UnfoldDataScience Maths part...
@71shubham3 жыл бұрын
How did we decide age splitting criterion?
@UnfoldDataScience3 жыл бұрын
Just for example I took here.
@januaralamien94213 жыл бұрын
XGBOOST = Algorithm or Framework? please explain
@UnfoldDataScience3 жыл бұрын
Internally a framework however the implementation is available in python hence we call algorithm.
@AhmadHassan-on6zq3 жыл бұрын
🙇♂️
@UnfoldDataScience3 жыл бұрын
:)
@tapaspal86234 жыл бұрын
Hi Aman Sir, Can you please explain how parallelism happens since it runs in sequencial manner. Next model requires previous models output. Thanks, Tapas
@UnfoldDataScience4 жыл бұрын
Hi Tapas, parallelism not in terms on model training , I was talking about parallelism in terms of hardware for example using multi cores of the processor, not to be confused with model training.
@YashSharma-xb2os4 жыл бұрын
Hi aman, please make a telegram group or whatsapp group where we can connect and join with you and ask queries.
@UnfoldDataScience4 жыл бұрын
I ll create Yash for sure.
@geetisudhaparida25233 жыл бұрын
From where 6 value has come??
@UnfoldDataScience3 жыл бұрын
Where 6? can u point me to timing in video?
@chanpreetsingh934 жыл бұрын
How to set or calculate gamma value?
@UnfoldDataScience4 жыл бұрын
Good question, Its subjective based on how model is behaving with Data, We can give a range and decide to tune it.
@navneetgupta46693 жыл бұрын
the learning rate was 72 (144/2). how did it change to 6?
@UnfoldDataScience3 жыл бұрын
Hi Navneet, can u let me know the time in the video, I will play and check that part.
@navneetgupta46693 жыл бұрын
@@UnfoldDataScience after 12:00 When you added another input (11). You took lambda as zero but forgot to square the numerator.
@avneshdarsh98803 жыл бұрын
output value calculated as the average of residuals in our case (4+8)/2=6
@bestcakesdesign3 жыл бұрын
Where are you working sir?
@UnfoldDataScience3 жыл бұрын
Hi Nikul, please ask queries related to Data Science only.
@rohitkaushik21722 жыл бұрын
Please don't copy the examples please present new examples