Tutorial 42 - Ensemble: What is Bagging (Bootstrap Aggregation)?

Bootstrap aggregating, also called bagging, is a machine learning ensemble meta-algorithm designed to improve the stability and accuracy of machine learning algorithms used in statistical classification and regression
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[@shubhamjindal39]

The best explanation of Bagging. I logged in just to write this comment down. Keep it up. Thanks a lot!

[@Moonlit_Bangtan]

The way you explained the concepts are very easy and understandable. Keep doing the same. Thanks a lot.

[@user-bj9hq7jf9o]

You the people again proved that KZbin is the best learning platform. Thank u so much sir for being a part of your KZbin student❤

[@vishesharora2352]

The video explains bagging extremely clearly. Thanks for the upload!

[@denissobczyk9363]

just take a moment and appreciate the brilliance of this guy! Once again saved me from reading countless pages...

[@mehnazmaharin1645]

please keep making videos. Your videos are easy to understand and clears up the concept!

[@shresthaditya2950]

0:06-Ensemble Techniques 0:30-Bagging is also called bootstrap aggregation 1:40-In Bagg we divide the data into various samples(on the basis of row sampling with replacement) depending on the number of ML models(Base Learners)known as Bootstrap and then the output which is in majority after running all the models is considered(Voting Classifier)also known as Aggregation 2:53-Row Sampling with replacement 4:02- 4:50-BootStrap 5:14-Aggregation

[@midhileshmomidi2434]

I think if I watch all your playlists I will definitely be confident that I will learn a lot

[@BalaMurugan-cb9ho]

Krish, this kind of videos are I looking for. The way you are teaching is very much understandable. Thanks for your videos

[@sonamde7507]

best explanation ever. You are really good at explaining things. Keep it up. looking forward to more detailed machine learning model videos. Thank you

[@sonal4]

you explained everything in a very simple language......i always watch your videos for machine learning....thank you

[@rajashekarappamadure8581]

sir, you are soo good at simplifying and explaining the complex topics

[@channaly2772]

I respect your simplicity and reserved a thumb up.

[@sidharthsingh3399]

This is just like "America got talent"/ "India got talent" show. Where we have participant(as data) performing his/her show and there are 4-5 judge(as model) and participant selection is based on judges review. Its one way perception to learn.

[@Kinglium]

thank you so much for your hard work! this is by far the best explanation I could grab my head around! keep up your good work!!

[@mansibisht557]

You're so good at explaining !

[@GuitarreroDaniel]

You are the only one that explained this right, thank you very much!

[@priyankakushwaha8407]

Thanks for explaining in simple words.

[@ManikandanRaju]

This is wonderful. Thank you for such a simple and easy understanding explanation sir.

[@sunilmali5380]

You are champ!! what an explanation. Thank you so much sir

[@brendachirata2283]

you just amazing, you have the sipmlest and clearest explanations

[@hydersal4073]

Greatly and deeply-explained. God bless and thanks a lot

[@_PremKharat]

One video and thats it concept clear Thanks a lot sir

[@ellentuane4068]

So easy to understand !!!! Thanks.. Greetings from Brazil

[@ayanmullick9202]

Thank you sir for easy explanation.

[@tanvirtanvir6435]

2:35 m

[@rkrish6476]

Great Explanation .. Thanks Krish

[@memonakhan9804]

This is actually a really nice explanation. Keep it up.

[@t-ranosaurierruhl9920]

Perfect explanation!

[@oguzcan7199]

It was an amazing explanation! Thank you a lot.

[@armansh7978]

thank you very much for very good explanation Krish , wish the best for you

[@nasreenbanu2245]

finally i got the concept.hats off sir..

[@sandipansarkar9211]

Superb video Keish. once again. Thanks

[@karthikannavarapu8436]

You are a god for the One day exam preparation students 🙏

[@sneharj2036]

Thanku so much sir for a wonderful explaination. Concept of Bootstrap Aggregation is very clear n nicely told. Your channel is very awsome, great videos.

[@keerthanavivin450]

Such a good explanation!

[@arvindsinha1]

Great explanation!

[@isratjahan207]

Nice explanation. Thank you!

[@harshpathak754]

Best lecture on bootstrapping

[@blackphillip5757]

You have a great channel man, keep up the good work.

[@sidkapoor9085]

Patrice o'Neal fan?

[@TheFofitas]

yes, very nicely explained. you are very clear, thank you! :)

[@baharehghanbarikondori1965]

Awesome tutorial on BAGGING

[@swatibogawat8368]

Ver Well and Simply explained Krish

[@rishi.m7160]

thanks a lot ur session was helpful .

[@suparnasaha3043]

Great explanation

[@deepakkumarthakur8429]

Loved it!!! 💙💙💙

[@trknigatu]

You have made it super clear. It shows your time investment on the subject. As the saying goes "If you can't explain it to a six-year-old, then you don't understand it yourself” Albert Einstein

[@manpreetsharma3846]

Amazing video!

[@anubhavnehru619]

Amazing explanation!

[@odelolatechup1447]

I love your videos thank you

[@karthikrajendran3394]

Well explained. Thank you. I was getting confused with the textual explanation.

[@rafipatel5020]

Sitting in my MSc AI class in London and watching him because he is just better!

[@rrrprogram8667]

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[@kin_1997]

amazing content, thank you !

[@samriddhlakhmani284]

PS : m>n is how it is. same sample is taken multiple times

[@devmani100]

Because we are doing the selection with replacement. Example :- Suppose you have a bag with 10 red balls, now you draw 5 balls at a time but with replacement. So after first draw you put all the 5 balls back in the bag now for the next draw you will have 10 balls in total. That's why

[@biswadeepdutta2225]

Gem of a tutorial!!!

[@prodyutdas1474]

you are awesome!! Thanks a lot.

[@aditya_01]

really nice video thank u

[@anjumanoj4703]

Nice explanation

[@yoshitha12]

Very clear ❤

[@rambaldotra2221]

Thanks A Lot Sir!!

[@codeandcurious]

Nice explaination

[@nandeeshkm3293]

thank you so much ❤

[@vaibhavkumar1509]

I am doing Master's in USA, thank you for this explanation.

[@abhinav02111987]

Excellent Krish.

[@anujasebastian8034]

Excellent!!!

[@fet1612]

This video was very informative, very well explained. Please keep helping students who need help. Be good and take care.

[@louerleseigneur4532]

Thanks Krish

[@imtiaznakib1040]

Really helpful

[@Pidamoussouma]

This is good explanation

[@shadiyapp5552]

Thank you ♥️

[@prashanths4455]

Awesome krish

[@pranjalgupta9427]

Thanks ❤

[@pazhaniarivarasu5741]

Thank you

[@Narsimhakhedkar]

Very well explained. I came here because my University professor totally messed up the explanation of this simple technique!

[@aarifansari9097]

thank you

[@arpanpradhan493]

Great Explaining! I am in Data Science Masters Program in Data mining class.

[@jananikannan6401]

Got a clear idea. One small suggestion. It would be nice if you explain the concepts based on some sample datasets consisting of a few rows and cols for explanation sake. That would give more detailed understanding.

[@utsapradhan7165]

Very good

[@_MubinShaikh]

Best video

[@aishwaryamundhe5070]

damn this is an excellent explanation

[@astaturinim3143]

Thanks

[@kmdkhaleeluddin6257]

Your explanation is awesome ❣️ and thank you so much for making these video for us I request you to provide your full notes of machine learning so it could be so easy for us ❣️✨it may possible to get high score in machine learning ❣️ Thank you once again ❣️✨.....

[@saying911]

Well Explained!! .Sir please make a video explaining the hyperparameter tuning of bagging regressor .How to decide the value of max_feature..

[@Ik1Wetniet]

for example we use decision trees with bagging. Which hyperparameters should we set to the trees being used in bagging (which minimum leaf size for the base learner should i choose?). also how many bags should i use ?

[@datascientist2958]

What is the difference between bagging, boosting, voting and stacking? Random Forest and XGB can be related with which method

[@muhammadumair1280]

Sir are you sure in this example data m

[@etikh404]

Sir, you are simply too much!

[@TJ-wo1xt]

nice one

[@radhay4291]

Good Explanation, is this m1,m2,m3 models are same classifiers or different classifiers

[@shubhamchaudhari1212]

When the base learners are created i.e different models, will the modles be only the ones that are comparible to solve Binary model as you said to cinsider the modle as Binary Regresion ?

[@RoshanKumar-fm2cn]

Wonderful

[@technojos]

Hi my dude.Thanks a lot for these videos.Every one of your videos is so special avesome.Can I have a question for you.Can u recommend a book which explains like you?

[@enchanted_swiftie]

Can we compare Bootstraping with Cross validation? In both techniques we take some part of the original data and giving to the model.

[@61_shivangbhardwaj46]

Thnx sir

[@ca177]

Love the enthusiasm, love the room presence.. I've seen 3 YT videos explaining Random Forest, and BAgging, this is the clearest one yet.. I still need to find why it is called Boot Strapping, what's the visual analogy that lead to this ??

[@vaddadisairahul2956]

bootstrap sampling is a statistical term where smaller samples are drawn without replacement from a larger original sample. That is the name attached here in machine learning. If you are asking for what is the literal meaning of bootstrapping, then google search gives the verb definition as "get (oneself or something) into or out of a situation using existing resources" so, here we are getting into using existing resources ( original dataset D). More in-depth I'm not sure sorry. If you get to know pls do comment

[@davidmccabe3054]

Great title

[@RajKumar-mndr]

Hi I am from none technical background and working in bpo in backend process not related to any technique, simply cut copy paste But looking forward for SAS ANALYTICS, PLS SUGGEST WHICH TECHNIQUE NEED TO LEARN, ALSO SHARE SOME VIDEOS LINK

[@prvn386]

Hi Krish,What If the voting classifer have equal number of 1 and 0 to classify from all the models. Then what will be the final output of classification??

[@sushanpatel2155]

Can i use GridSearch inside a BaggingClassifier for decision tree estimator inorder to choose a proper hyperparameter for max_leaf_nodes.