Machine Learning with R: Random Forest Classification Approach
Random Forest is an ensemble machine learning technique used for both classification and regression tasks. It is based on the concept of decision trees, where multiple decision trees are trained on different subsets of the data, and their predictions are combined to produce a more accurate and robust final prediction. This workshop will go over the theory of Random Forests and then provide attendees with hands-on training on conducting Random Forest classification, training the data, testing accuracy, and working with tuning parameters.
Workshop Offerings
March 2024
Facilitator Bio
Amirreza is a Master’s student in the Electrical and Computer Engineering department of McMaster University with 8 years of experience in different programming languages.
Workshop Files
- Random_forest_workshop.Rmd
- test.csv
- train.csv
January 2023
Facilitator Bio
Presentation by Shaila Jamal, DASH Support Assistant and PhD Candidate in Earth, Environment, and Society.
This event was run in collaboration with the YWCA’s Uplift Program, which supports women and non-binary people re-skilling to enter the tech industry.
Workshop Files
- DASH-Workshop- Random Forest Classification.Rmd
- telecom_customer_churn.csv