Comparative Analysis of Supervised Algorithms With Hyperparameter Tuning on Breast Cancer Dataset
Yagya Buttan, Alka Chaudhary, Komal Saxena, Samriddh Kohli, Ajay Rana · 2021
In a fast-pacing and the advancing world where setting newer targets each day and working for them has become the next big thing in the scenery, we as humans have taken our health factor on second foot or to be honest, we have now become far more lenient and sooner or later we need to pay the price. We have shed shelter to many new diseases, in turn making us pay the price for it. Amongst those, one which has drawn attention from the past few years is Breast Cancer (BC). As a result, we have seen a major increase in the death rate for women especially. It can be broadly be divided into categories Malignant and Benign. Benign is termed as noncancerous (that means it cannot spread to other parts of the body) but, Malignant is the contrary of benign. If it is spotted at the right time treatment can be done. The data-set we are using here is fetched from Wisconsin Cancer data-set. Through the medium of this paper, we aim at using certain machine learning algorithms and python that are tested for best-case parameters(sequentially), to come up with a conclusion for which machine learning algorithm fits best over here in analysing the given cancer data-set for Benign(B) and Malignant(M).