Hindi- Ensemble Techniques-Bagging Vs Boosting|Krish Naik

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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[@ankurnayak7649]

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

[@VyomKumaraes]

Sir pls upload more lecture we are waiting pls!

[@thespeedyronaldo]

3:55 Start

[@sidnoga]

Sir please make a video on ada boost and xgboost

[@AnkurKhatri-b7t]

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

[@amitchauhan5924]

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]

you are correct single type of algorithm possible in bagging

[@prernasrivastava6524]

Sir pls continue making videos

[@altafshaikh3900]

Great lecture overall 🙏🏻

[@yashjain4630]

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

[@himanshuAIML]

Best explanation ❤

[@maths_impact]

Wonderful

[@tariniashritacharya1386]

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

[@vivekkumar-ij3np]

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.

[@tanmaychakraborty7818]

Sir pca ka Theoretical lecture bhi karwa do please

[@umeshsamal165]

Sir pls upload more lecture we are waiting pls!

[@chroniclesofcode]

Sir apka hindi lecture padane ko acha lagata hai.

[@123vijit]

please make video on hyperparamter

[@krishnaikhindi]

Already made

[@pkumar0212]

👌

[@suparnasaha3043]

Great explanation

[@Varsha_Bankawat]

Bagging can be considered as model for further analysis?

[@deepaklonare9497]

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

[@fahadnasir1605]

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

[@shilpawaybhase8573]

Please upload boosting next video

[@surajprakash4630]

Very helpful video sir...thanks a lot

[@gaurigupta4766]

Wow😊best explanation

[@rockfighter9974]

Nice video thank you

[@Satvikshukla0007]

Very nice sir

[@shubhamSharma-gw1oe]

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

[@niharranjansamal8263]

Yeah bro same here 🙋🏻‍♂️

[@sheikhshah2593]

Great 🔥🔥

[@krishnabhutada3983]

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?

[@krishnaik06]

there is a seperate l;ibrary for random forest in sklearn

[@mdmd-un2gd]

@@krishnaik06 big big heart from bangladesh , Your are videos are top notch , i am in 9 standard .

[@letstry2854]

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