Meanwhile rahul watching rahuls interview prediction to pass aiml exam 😂

@because2022

25 күн бұрын

Hehehe..

Good work. Add few problems when u have time

@because2022

Жыл бұрын

Thanks. The problem is we dont have much problems in the foreign author book. If you have some specific problems please mail to me venkat.kvhapp@gmail.com

Thank You Sir for this understandable ensemble learning ❤

@because2022

17 күн бұрын

Most welcome keep learning.

@TheQueen-oj7os

17 күн бұрын

@@because2022 🤍

Thanks for this video 🔥🎊👏

@because2022

3 ай бұрын

Most welcome. Keep learning and keep sharing with your friends and juniors.

Easily understood sir ❤

@because2022

17 күн бұрын

Do well.

Thank you sir ❤

@because2022

Жыл бұрын

Welcome

@zeus999ff3

17 күн бұрын

Avanga mam thana bro 🙄

Mam ippa model 2 la no result varutha atha base panni tha new traing data form pananuma

@because2022

Жыл бұрын

We create new data based on output frm all models.

Sir...first data 70,20 ku YES NO YES vandhuchu...so ground truth yes uh so second data 80,30 ku enna predictions varum?

@because2022

Жыл бұрын

No Yathindra. Ground truth is always from table. For new training data alone, we take predictions from all models then include ground truth also.

Sir advanced ensemble technique la blending nu innoru concept iruku adhu teach panalaya sir?

@because2022

Жыл бұрын

Hi Malarkodi, it got missed. You can refer this. www.scaler.com/topics/machine-learning/blending-in-machine-learning/ . You can let me know if you dont understand. Then we can discuss.

@malarkodi720

Жыл бұрын

@@because2022 thank you sir

@SivaSelvaraj-fu4wf

17 күн бұрын

​Sir aana anna university syllabus lah blending illa aama thaana sir​@@because2022

9:37 sir boosting use panna overfitting nadakum sonninga overfitting na excellent ta work aagum la sir...adhuku yen sir namma boosting use panni reduce pannurom adhu nalla dhana sir work aagudhu aprm yen use pandrom

@because2022

2 ай бұрын

It will work well only on training data and not on any new data. Thats why.

@Usadreamerok

Ай бұрын

Over fitting means not enough training dataset its a negative thing bro

@because2022

Ай бұрын

@@Usadreamerok It need not mean about amount of training data.