This channel has become one of my favorite platforms to learn ml, owing to the crisp explanation by Aman.

Nice one here. Thank you for the simplicity employed in explaining the core concepts.

Excellent explanation

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

Looking at the content & no. Of subscribers. Highely underrated

listen to this video 3 times.. lot of insights. Thank you.

YOU ARE TRUE KNOWLEDGE

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

Gr8 video Sir. You have explained it clearly and in a very simple way. Thanks a lot 🙏

one of the best explanation on complex intuition of XG Boost.....

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!

Superb simple explanation, Thank you very much

Definitely best & most understandable explanation of XGB🔥

Very nicely explained, Thanks Sir. One of the best videos I have seen on KZfaq.

Very NIce Explanation!

very nice video.brief..concise..to the point..agree with others..probably the best explanation so far on youtube .way to go bro

Thanks a lot for this. Very helpful for me as I am brushing up on ML theory for interviewing. Awesome work!

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.

Excellent explanation.

Very good Aman

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)

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?

Hey good one again! Continue your good work.. Thanks

Awesome indepth explanantion, keep up the good work man!

Q1. how to interpret Similarity Score. Q2. what is meaning of High Similarity Score and Low Similarity Score

finished watching

12:01 why not square of sum of residuals as u said in the formula?

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.

This the best vedio in XGboost.

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?

Thanks for the excellent work!!

Hi Aman,thanks for the video,please explain how lambda controls overfitting

I like the way u explain complex concepts in simple way. Thanks

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

can u do one for xgboost classification

can you just describe the loss function for it ?

Straight to the point. Thanks

Thanks a lot brother..god bless u for ur information

one question- what is the output in the last formula of new prediction. which output it is?

thank you could you explain how to do feature important using XGboost

Great explanation

great explanation

you are a god...

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.

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

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

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)

Is the same procedure for classification?

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

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.

too good...!!

Very clear ! Thank you !✨🙏

Can we apply L1 and K2 regularization technique to any algorithms whether its Linear regression, xgboost, gboost, random forest or etc?

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.

From where is output 6 coming? The similarity score for 2nd branch was 72 according to your formula. I fail to understand please help

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 ?

Do we calculate IG and Entropy for splitting criteria?

Hai Aman, can you please tell us, why the data should be normally distributed. and how does it affects the ML models?

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?

Hey Aman, you talked about missing value treatment in XG boost in your previous video..how does XG boost treat missing values?

good explanation :)

Can we generate the mathematical equations between adopted inputs and output parameters after successful implementation of xgboost?

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

Awesome explanation 👍 although it was bit complicated. can you create videos on poisson regression and survival analysis?

Sir, Can you please how this xgboost works for logistic regression.

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.

Please make vedio on "Pipeline" of building model and how it is implement in Production

14:22 , I think output is (12) square, 144/2+0, 72. Please Correct me if wrong...

Sir nice video sir.pls make a video of the dependent variable is categorical that is yes or no .

Sir ,how 6 is coming ,u missed to square the sum of 4 and 8, please tell me

sir, please do cover light GBM and is advantage over XGboost

is knowing the math behind algorithm must or just knowing that how algorithms works is enough? please please please give a reply.

hi can u pls explain how the output is 6 for the second observation in the table?

Where is gradient descent happening in the algorithm?

Can you make end to end clustering algorithm. How to select variable, no of clusters and then final deployment

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

At 7:19 is it not Age < 10 instead of Age > 10?

Hi Aman!!!, have a question, how can i predict a gender with mobile data phone with an XGBoost algorithm

i dont understand formula: firstly u used (sum of res square) & secondly u used only (sum of res) what is reason?

Are all models M1, M2 etc. the same model, data and tree and features used?

Hi Aman it's only change one residual i.e 2.2 what about remaining how we get remaining residuals ?

sir please make vedios on RNN ,LSTM

In new prediction, which value you take as output?

How did we decide age splitting criterion?

XGBOOST = Algorithm or Framework? please explain

🙇‍♂️

Hi Aman Sir, Can you please explain how parallelism happens since it runs in sequencial manner. Next model requires previous models output. Thanks, Tapas

Hi aman, please make a telegram group or whatsapp group where we can connect and join with you and ask queries.

From where 6 value has come??

How to set or calculate gamma value?

the learning rate was 72 (144/2). how did it change to 6?

Where are you working sir?

Please don't copy the examples please present new examples