Bagging | Introduction | Part 1
42.3 هزار بار بازدید -
4 سال پیش
-
Bagging, or Bootstrap Aggregating, is
Bagging, or Bootstrap Aggregating, is an ensemble method that involves training multiple models independently on different subsets of the training data. These models are then combined through averaging or voting to make predictions. Bagging reduces variance and improves the stability and accuracy of the final model, making it a popular choice in machine learning.
Code used: github.com/campusx-official/bagging-ensemble
Bias Variance Tradeoff: • Bias Variance Trade-off | Overfitting...
============================
Do you want to learn from me?
Check my affordable mentorship program at : learnwith.campusx.in/s/store
============================
📱 Grow with us:
CampusX' LinkedIn: www.linkedin.com/company/campusx-official
CampusX on Instagram for daily tips: www.instagram.com/campusx.official
My LinkedIn: www.linkedin.com/in/nitish-singh-03412789
Discord: discord.gg/PsWu8R87Z8
E-mail us at [email protected]
⌚Time Stamps⌚
00:00 - Intro
00:25 - Plan of attack
01:50 - What is Bagging
14:32 - Code Demo
30:50 - Outr
4 سال پیش
در تاریخ 1399/12/22 منتشر شده
است.
42,333
بـار بازدید شده