  In this paper, we analyze the Wisconsin Diagnostic Breast Cancer Data using
Machine Learning classification techniques, such as the SVM, Bayesian Logistic
Regression (Variational Approximation), and K-Nearest-Neighbors. We describe
each model, and compare their performance through different measures. We
conclude that SVM has the best performance among all other classifiers, while
it competes closely with the Bayesian Logistic Regression that is ranked second
best method for this dataset.
