Description
Q1) Bagging and Boosting are two methods for ensemble learning algorithms.
Please answer the following:
a. What is Random Forest?
b. What is Boosting?
c. Explain in your own words the difference between Random Forests and
XGBoost.
Q2) In this part, you will experiment the difference in the behavior of Random
Forests and C4.5 Decision Tree. You have to report the Accuracy, Precision,
Recall, F-Score, Bias, and the Variance of each.
Use the following binary-class dataset
(https://www.dropbox.com/s/8r3nfghlhg96n63/COMP4388-
Dataset1.csv?dl=0). Split the data 80 training/20 test and explain the steps of
your work (i.e., if you scale data, how did you split, the performance measures,
bias and variance, and state your findings in your own words). You have to
provide your Python code as well.
This is an individual assignment. Please make sure that the submission of this
assignment is as a reply to the message sent on Ritaj and make sure that the file
is names “A3-XXXXXXX.docx” where XXXXXXX is your student ID.
