Hindi- Ensemble Techniques-Bagging Vs Boosting|Krish Naik

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Ensemble methods are techniques that create multiple models and then combine them to produce improved results. Ensemble methods usually produces more accurate solutions than a single model would. This has been the case in a number of machine learning competitions, where the winning solutions used ensemble methods.
#bagging #boosting #ensemble
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Опубликовано:

28 июн 2022

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Комментарии : 34

@ankurnayak7649 Год назад

Bahut sahi laga sir ,English sa hindi mai aa gay bahut bahut dhanywaad

@surajprakash4630 Год назад

Very helpful video sir...thanks a lot

@altafshaikh3900 Год назад

Great lecture overall 🙏🏻

@letstry2854 Год назад

Hey Krish, I am enjoying learning ML through your video. Please add more content!!

@thespeedyronaldo Месяц назад

3:55 Start

@gaurigupta4766 7 месяцев назад

Wow😊best explanation

@suparnasaha3043 Год назад

Great explanation

@sheikhshah2593 8 месяцев назад

Great 🔥🔥

@himanshuAIML Год назад

Best explanation ❤

@chroniclesofcode 2 года назад

Sir apka hindi lecture padane ko acha lagata hai.

@VyomKumaraes 2 года назад

Sir pls upload more lecture we are waiting pls!

@user-wk8oc1so3p Год назад

Just want to know how Max voting classification will work if we built even number of Models in Bagging and Number of Output of both 0 and 1 are same. What will be the final Output

@fahadnasir1605 Год назад

Hi Krish, did you upload the videos for Adaboost XGboost. I didn't find it in the ML playlist

@rockfighter9974 Год назад

Nice video thank you

@maths_impact Год назад

Wonderful

@prernasrivastava6524 2 года назад

Sir pls continue making videos

@deepaklonare9497 Год назад

what about the training data+classfication problem ? and for training data+regression problem ? Both will have AVG only?

@sidnoga Год назад

Sir please make a video on ada boost and xgboost

@tanmaychakraborty7818 Год назад

Sir pca ka Theoretical lecture bhi karwa do please

@yashjain4630 7 месяцев назад

Sir Bagging is a Homogeneous Model , So how can you say we can Use Multiple Type of Model In Bagging ?

@varshabankawat2890 5 месяцев назад

Bagging can be considered as model for further analysis?

@amitchauhan5924 10 месяцев назад

Hello Sir, how we can use different algorithms at once in bagging? As I read on other materials, we can use one type of models for all base models with different data. Please explain.

@rushabhtijare7188 7 месяцев назад

you are correct single type of algorithm possible in bagging

@shubhamSharma-gw1oe 2 года назад

sir please upload remaining video hurry up because our placement session will start in September. Thank you

@niharranjansamal8263 2 года назад

Yeah bro same here 🙋🏻‍♂️

@shilpawaybhase8573 Год назад

Please upload boosting next video

@123vijit 2 года назад

please make video on hyperparamter

@krishnaikhindi 2 года назад

Already made

@tariniashritacharya1386 2 года назад

Sir, do we provide entire dataset for boosting model? Or like bagging we provide subsets?

@vivekkumar-ij3np 2 года назад

For boosting, we provide entire dataset to 1st model then again that dataset is provided to next model but the second model know, how many error you have done while training in 1st model. This happen till last.

@krishnabhutada3983 2 года назад

Krish, is there any library in sklearn for bagging other than Random forest as it uses Decision Trees...Or do we have to test and train individually as we use?