This project trains and tests a regularized logistic regression model on two data sets, namely the breast cancer and sonar data sets which are available here: https://www.csie.ntu.edu.tw/~cjlin/ libsvmtools/datasets/binary.html#breast-cancer and here: https://www.csie.ntu.edu. tw/~cjlin/libsvmtools/datasets/binary.html#sonar. “xxx-scale-test-indices.txt” should contain the train indices, and “xxx-scale-test-indices.txt” should contain the indices for testing.
The project is divided into the following tasks: -Use the 5-fold cross validation method to decide the best value of the parameter C. The best C is the one that yields the lowest validation error. – Use the selected best C value to train a logistic regression model on the whole training data and evaluate and report its performance (by error rate) on the testing data. – Report the results on the two data sets.
Repeat the same experiment as above using linear, RBF and polynomial kernel SVMs.