Python for Machine Learning (ML) 5: Bagging and Boosting Algorithms (IN-PERSON)
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Python for Machine Learning (ML) 5: Bagging and Boosting Algorithms (IN-PERSON) In-Person
IN-PERSON (Library North Building, Second Floor, Library Classroom 2 computer lab)
GSU Data Ready! Badges Micro-Credentialing: https://lib.gsu.edu/data-ready
This workshop introduces participants to Bagging and Boosting Algorithms using Python.
WORKSHOP TOPICS:
-- Bagging, also known as Bootstrap aggregating, is an ensemble learning technique that helps to improve the performance and accuracy of machine learning algorithms. It is used to deal with bias-variance trade-offs and reduces the variance of a prediction model. Bagging avoids overfitting of data and is used for both regression and classification models, specifically for decision tree algorithms.
-- Boosting algorithms combine multiple weak learners in a sequential method, which iteratively improves observations. This approach helps to reduce high bias that is common in machine learning models.
Prerequisites:
-- Python for Machine Learning (ML) 4: Decision Trees - live workshop calendar posting: https://rooms.library.gsu.edu/event/11683477
NOTE: Please read our Workshops ~ Etiquette & Policies page for pertinent information to your workshop attendance.
Presenter(s): Ufuk Seçilmiş, M.S. Candidate in the Mathematics & Statistics Department and Graduate Research Assistant with the Library's Research Data Services Team
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- Date:
- Friday, March 8, 2024
- Time:
- 2:00pm - 3:30pm
- Time Zone:
- Eastern Time - US & Canada (change)
- Location:
- Classroom 2, Library North 2
- Campus:
- Downtown Campus Library
- Categories:
- Data Services Workshops