Introduction to Ensemble learning|What is Ensemble Modelling|Understanding Ensemble Models

[@jugalbhatt7475]

There cannot be any better explanation than this. Thank you .

[@UnfoldDataScience]

Thanks a lot Jugal. Your comments are my motivation.

[@Sagar_Tachtode_777]

Very well ensembled the concepts. You just nail the big concepts with a simple example. Thank you. Keep it UP. Grow fast and furious!!!

[@UnfoldDataScience]

Thanks a ton Sagar for motivating me.

[@georgechristy11]

i got my internship because of this video!!

[@popk814]

As someone who is knew to machine learning, this was very easy to follow and understand, thank you

[@UnfoldDataScience]

Thanks a lot for motivating me through your comments.

[@malavikadutta1011]

Aman your way of explaining Data Science algorithms is outstanding.

[@UnfoldDataScience]

Thanks Malavika.

[@sanjeevkmr5749]

Kudos to your work !!! Your way of explaining things is easily understandable. Keep doing the good work. Thanks!!!

[@UnfoldDataScience]

Welcome Sanjeev. Thanks for watching.

[@adolfomichelena]

Perfect explanation Aman. You got a follower from latin america. Thank you.

[@UnfoldDataScience]

Thanks and welcome Adolfo :)

[@bangarrajumuppidu8354]

way of explanation is outstanding!!!

[@UnfoldDataScience]

Thanks Bangarraju.

[@komuna5984]

Thanks a lot for creating this playlist. You are a great instructor indeed. :)

[@UnfoldDataScience]

Thanks a lot.

[@RameshYadav-fx5vn]

Nice and simple explanation. Thank you.

[@UnfoldDataScience]

Welcome.

[@mazenomar7279]

Awesome.many thanks keep moving.

[@UnfoldDataScience]

Thank you Mazen, I will

[@preranatiwary7690]

Nice articulation

[@UnfoldDataScience]

Thank you.

[@mamataparab9803]

What a great explanation! I have gone through so many videos, documents, and articles but have not seen such an easy explanation. Thank you so much

[@UnfoldDataScience]

Glad it was helpful!

[@sandipansarkar9211]

great explanation

[@UnfoldDataScience]

Glad you think so :)

[@deshapriyamukherjee]

Awesome explanation brother. God bless you.

[@UnfoldDataScience]

Thanks for watching Deshapriya.

[@deshapriyamukherjee]

@@UnfoldDataScience it's my pleasure.

[@tejaswidalavi9269]

Very Helpful !! Thank you Sir.

[@UnfoldDataScience]

Most welcome Tejaswi.

[@khanyasser]

Great video! Thank You!

[@UnfoldDataScience]

Thanks Yaseer.

[@sandipansarkar9211]

Finished watching

[@vikaskulshreshtha]

Aman please post the video for support vector machine

[@UnfoldDataScience]

Will do Vikas. Thanks for suggesting.

[@sumitkumar89909]

very helpful thanks

[@UnfoldDataScience]

Glad it helped

[@amitprabhakar7541]

fantastic expalination of concepts , I am searching for unsupervised learning (USL) videos , can you tell me have you uploaded it or not, because I could not find it

[@UnfoldDataScience]

Yes I have done, Clustering/Association analysis/recommendation engine etc . You can find with topic keyword or in my playlist.

[@yashgupta7620]

Sir please algorithms pe video banie deep me samjhake long video . In short videos se jyada help nhi hopati we get only basic idea but not indepth idea ... Hope u understand sir and u are doing excellent job keep growing sir .. keep helping in us 🔥

[@UnfoldDataScience]

Thanks Yash. Keep watching. Detailed videos on the way .

[@gopiramankumar2231]

Nice video 👍👌

[@UnfoldDataScience]

Thanks a ton.

[@tridipdas5445]

Very nice explanation in simple and effective way. But I think one mistake you have made. You have told the type of models in Bagging can be different from each other. What I believe is that the subsets of data is different,but the base model should be same. Correct me if I am wrong. Overall , the video is perfect. Keep sharing knowledge!

[@UnfoldDataScience]

Thanks a lot. Let me check on your feedback. :)

[@GopiKumar-ny3xx]

Nice explanation...

[@UnfoldDataScience]

Thanks a lot.

[@Sidcr07]

hi sir..i am loving your videos a lot..can u plzz make a video on plotly dash and webscrapping..plz..i have searched a lot but i didnt find the satisfaction tht i get after ur explanation.

[@UnfoldDataScience]

Okay sure

[@radheyelectrical8826]

Very good bhai

[@UnfoldDataScience]

Thanks Rajeev.

[@mazenomar7279]

Are you in each bag split data into train and test?sorry for beginner quiz.

[@UnfoldDataScience]

No, only from train.

[@mazenomar7279]

@@UnfoldDataScience many thanks

[@sunnyarora4916]

NIce video, you said in stacking video that bagging and boosting come under homogenous ensembles,does that mean here M1 M2..M4 will be the same algorithm?

[@UnfoldDataScience]

Yes, correct. Usually same.

[@koushikr182]

Hi Aman, thanks for your videos, they are really good and easy to understand. So is boosting opposite to Bagging meaning is it without replacement as opposed to Bagging?

[@ravanshyam7653]

can we combined one decision tree model ,logestic ,knn to get ensemble olp is like dis possible sir?

[@UnfoldDataScience]

I think only one type.

[@harishashankar3661]

Thank you Aman. This was easier to understand. Question - how do we define the num of bags? And since these are with replacements, won’t some rows(from 100) not be considered at all in all bags?

[@UnfoldDataScience]

Thanks Harisha, Number of bags is determined by "n_estimators " in sklearn.python. Default is 500 however we can change. Answer to second question is, in bagging it is possible that some records might get repeated in multiple bags however there will be sufficient difference if you compare many bags. thats the purpose.

[@kirtisardana8479]

Thanks Aman for wonderful explanation ....question : whether these models (M1..M4) should be from same algorithm ? If not how to identify which algorithm to implement ....considering there are lot of different algorithms.

[@UnfoldDataScience]

Hi Kirti, Usually same algorithm. Otherwise the learning becomes little complex .

[@rameshmamilla5392]

Good one Aman! Let's say my each individual models has come with good accuracy, however by the bagging the model accuracy will be less as compare to the individual model, so how will be treating this situation?

[@UnfoldDataScience]

Thanks Ramesh. Yes it is possible at times than u will observe a drop in accuracy in combined model however what we intent to give to business is more robust model. Little less accuracy is fine but model should be more flexible and robust. This is exactly what we try to achieve through ensemble.

[@rameshmamilla5392]

Yes agree Aman, Thanks!

[@yewensong1708]

Hi, you're fantastic at explaining these obscure concepts in layman's terms, I absolutely love it thank you so much, can you please make a video talking about the hyper parameters of xgboost like the number of parallel trees and and number of estimators etc, I have trouble linking the parameters to the maths behind it. Thank you so much

[@UnfoldDataScience]

Great suggestion! Sure.

[@aliounethiam1778]

Discours sur l'homme....

[@aliounethiam1778]

B1 no ossama...

[@arshalam9592]

PLZ UPLOAD DEEP LEARNING VIDEOS

[@UnfoldDataScience]

Hi Ashab, I will go on all topics one by one. Now I am starting detailed NLP videos on learner's demand. Keep watching :)

[@arshalam9592]

@@UnfoldDataScience Thanks for reply , your work is appreciable & great to see you again .

[@lampcamper6604]

agar dost hi harami ho toh ? uske lie koi ensemble model hai?

[@SharadAgnihotri3093]

Please activate your join button...

[@UnfoldDataScience]

www.buymeacoffee.com/amanrai77J Join button soon

[@raiaashish]

Are you a Tom Cruisese fan??? 😁

[@UnfoldDataScience]

Yes :D