Breast cancer classification problem

At the moment supervised learning is the most common class of ML problems in the business domain. In Chapter 1, Predict the Class of a Flower from the Iris Dataset, we approached the Iris classification task by employing a powerful supervised learning classification algorithm called Random Forests, which at its core depends on a categorical response variable. In this chapter, besides the Random Forest approach, we also turn to yet another intriguing yet popular classification technique, called logistic regression. Both approaches present a unique solution to the prediction problem of breast cancer prognosis, while an iterative learning process is a common denominator. The logistic regression technique occupies center stage in this chapter, taking precedence over Random Forests. However, both learn from a test dataset containing samples with predetermined measurements and compute a prediction on new, unseen data.

Before we proceed further, a quick note...