Logistic Regression and Adaboost for Classification
In this assignment of CSE472 course we implement adaboost algorithm with
logistic regression as weak learner to predict the binary labels of given
datasets.
Datasets Used
The first two datasets are saved in this repo. The third one is too large to
push to GitHub, so you'll need to download it yourself.
wget -O creditcard.zip https://www.kaggle.com/mlg-ulb/creditcardfraud/download
unzip creditcard.zip
mv creditcard.csv ./data/
Report of Logistic Regression
Telco dataset
Performance measure
Training
Test
Accuracy
80%
79%
True positive rate
53%
50%
True negative rate
89%
89%
Positive predictive value
65%
63%
False discovery rate
34%
36%
F1 score
58%
56%
Adult dataset
Performance measure
Training
Test
Accuracy
84%
84%
True positive rate
58%
58%
True negative rate
92%
92%
Positive predictive value
72%
71%
False discovery rate
27%
28%
F1 score
64%
64%
Credit Card Fraud dataset
Performance measure
Training
Test
Accuracy
42%
42%
True positive rate
94%
93%
True negative rate
41%
41%
Positive predictive value
03%
03%
False discovery rate
96%
96%
F1 score
07%
07%
Report of Adaboost
Telco dataset
Number of boosting rounds
Training
Test
5
79%
78%
10
81%
79%
15
80%
79%
Adult dataset
Number of boosting rounds
Training
Test
5
84%
84%
10
84%
84%
15
84%
84%
Credit Card Fraud dataset
Number of boosting rounds
Training
Test
5
78%
78%
10
98%
98%
15
97%
97%