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

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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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Комментарии : 204

@shubhamjindal39 4 года назад

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

@Moonlit_girl73845 4 года назад

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

@VaralakshmiAllu-g5t 4 месяца назад

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

@sidharthsingh3399 3 года назад

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.

@vishesharora2352 3 года назад

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

@denissobczyk9363 Год назад

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

@tanvirtanvir6435 Год назад

2:35 m

@midhileshmomidi2434 5 лет назад

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

@channaly2772 2 года назад

I respect your simplicity and reserved a thumb up.

@mansibisht557 3 года назад

You're so good at explaining !

@shresthaditya2950 Год назад

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

@samriddhlakhmani284 4 года назад

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

@devmani100 3 года назад

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

@sonal4 3 года назад

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

@ellentuane4068 3 года назад

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

@rkrish6476 Год назад

Great Explanation .. Thanks Krish

@mehnazmaharin1645 4 года назад

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

@singwithnoma 22 дня назад

Woow your an amaizing teacher, Well done i have understood the concepts thank to you thank you very much.

@priyankakushwaha8407 2 года назад

Thanks for explaining in simple words.

@karthikannavarapu8436 9 месяцев назад

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

@BalaMurugan-cb9ho 4 года назад

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

@brendachirata2283 2 года назад

you just amazing, you have the sipmlest and clearest explanations

@nasreenbanu2245 2 года назад

finally i got the concept.hats off sir..

@sunilmali5380 4 года назад

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

@arvindsinha1 11 месяцев назад

Great explanation!

@sonamde7507 4 года назад

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

@_PremKharat Год назад

One video and thats it concept clear Thanks a lot sir

@baharehghanbarikondori1965 3 года назад

Awesome tutorial on BAGGING

@ManikandanRaju 2 года назад

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

@rafipatel5020 11 месяцев назад

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

@rajashekarappamadure8581 3 года назад

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

@suparnasaha3043 2 года назад

Great explanation

@karthikrajendran3394 Год назад

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

@harshpathak754 3 года назад

Best lecture on bootstrapping

@hydersal4073 3 года назад

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

@swatibogawat8368 4 года назад

Ver Well and Simply explained Krish

@trknigatu Год назад

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

@sneharj2036 2 года назад

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.

@memonakhan9804 Год назад

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

@Kinglium 3 года назад

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!!

@sandipansarkar9211 4 года назад

Superb video Keish. once again. Thanks

@vaibhavkumar1509 Год назад

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

@GuitarreroDaniel 3 года назад

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

@ayanmullick9202 2 года назад

Thank you sir for easy explanation.

@rishi.m7160 3 года назад

thanks a lot ur session was helpful .

@t-ranosaurierruhl9920 4 года назад

Perfect explanation!

@armansh7978 4 года назад

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

@anjumanoj4703 3 года назад

Nice explanation

@keerthanavivin450 3 года назад

Such a good explanation!

@codeandcurious 2 года назад

Nice explaination

@odelolatechup1447 9 месяцев назад

I love your videos thank you

@Narsimhakhedkar 2 года назад

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

@oguzcan7199 2 года назад

It was an amazing explanation! Thank you a lot.

@anujasebastian8034 3 года назад

Excellent!!!

@rakeshp8711 4 года назад

Thank you for the explanation. It is not necessary that m should be less than n. It can be equal as well.

@chandrashekharpujari167 4 года назад

You are absolutely right bro, In bagging we do not subset the training data into smaller chunks and train each tree on a different chunk. Rather, if we have a sample of size N, we are still feeding each tree a training set of size N (unless specified otherwise). But instead of the original training data, we take a random sample of size N with replacement. For example, if our training data was [1, 2, 3, 4, 5, 6] then we might give one of our trees the following list [1, 2, 2, 3, 6, 6]. Notice that both lists are of length six and that “2” and “6” are both repeated in the randomly selected training data we give to our tree (because we sample with replacement).

@prodyutdas1474 3 года назад

you are awesome!! Thanks a lot.

@isratjahan207 3 года назад

Nice explanation. Thank you!

@abhinav02111987 4 года назад

Excellent Krish.

@TheFofitas 2 года назад

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

@yoshitha12 Год назад

Very clear ❤

@arpanpradhan493 7 месяцев назад

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

@prashanths4455 3 года назад

Awesome krish

@aditya_01 3 года назад

really nice video thank u

@rambaldotra2221 3 года назад

Thanks A Lot Sir!!

@fet1612 5 лет назад

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

@jananikannan6401 2 года назад

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.

@blackphillip5757 3 года назад

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

@sidkapoor9085 3 года назад

Patrice o'Neal fan?

@louerleseigneur4532 3 года назад

Thanks Krish

@pranjalgupta9427 2 года назад

Thanks ❤

@imtiaznakib1040 3 года назад

Really helpful

@nandeeshkm3293 2 года назад

thank you so much ❤

@kin_1997 2 года назад

amazing content, thank you !

@_MubinShaikh 3 года назад

Best video

@deepakkumarthakur8429 2 года назад

Loved it!!! 💙💙💙

@rrrprogram8667 4 года назад

Subscribed.... For ur dedication

@Pidamoussouma 4 года назад

This is good explanation

@sridhar6358 3 года назад

so to say a) a fit will be done with the model sample data of each model b) and there will be n such models for which fit will be done c) and prediction also will be done on each model and the d) result of each models prediction will be averaged if it is regression problem and e) voting shall be made if it is classification - how does voting go when we have a test set of data , we would have all the cases in the test model in case of classification then how does voting take place

@manpreetsharma3846 3 года назад

Amazing video!

@biswadeepdutta2225 3 года назад

Gem of a tutorial!!!

@kmdkhaleeluddin6257 Год назад

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 ❣️✨.....

@muhammadumair1280 3 года назад

Sir are you sure in this example data m

@shadiyapp5552 Год назад

Thank you ♥️

@utsapradhan7165 3 года назад

Very good

@pazhaniarivarasu5741 4 года назад

Thank you

@alihussien7935 7 месяцев назад

Thanks for all your good video. Your explanation are very good. But you don't tell ps , when we used and why. What is the goal?

@aishwaryamundhe5070 3 года назад

damn this is an excellent explanation

@TJ-wo1xt 2 года назад

nice one

@thirupathireddy6149 5 лет назад

Krish...Explain how to reduce an error for Regression and classification models ,,,Thanks

@aakashsinghrawat3313 4 года назад

go through EDA and feature engineering part, maybe improving them may reduce error.

@vijethrai2747 4 года назад

Understand the mathematics behind the models

@RajKumar-mndr 5 лет назад

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

@astaturinim3143 2 года назад

Thanks

@davidmccabe3054 4 года назад

Great title

@daohoang5973 2 года назад

Indian guys literally save the rest of the world and do the explanation job better than my teacher !

@etikh404 4 года назад

Sir, you are simply too much!

@saying911 4 года назад

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

@Noonewknows 8 месяцев назад

Could you teach us how to calculate uncertainty in regression model for each test data set

@anime_on_data7594 3 года назад

What is pasting ? Can you please expalne more about resampling and replacemt

@radhay4291 3 года назад

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

@RoshanKumar-fm2cn 4 года назад

Wonderful

@61_shivangbhardwaj46 3 года назад

Thnx sir

@s.shanmugapriyacse7044 2 года назад

Very good explanation. A doubt if so one could clarify it will be help ful.. In Testing data set , will it also be given different test data for each model

@self-made-datascientist1181 4 года назад

I dont get why not train all the models with all the training data available? If you seperate the data randomly the original distribution will get affected and models will be randomly good or bad depending on how lucky they were to get a close distribution in the splitting

@pratikbhansali4086 3 года назад

With whole data only one model can be created bro if we chose a sample from a given dataset then only everytime our model will give slightly different results so we combine the results of diff model