Simple Binary Classification for Breast Cancer with Ensemble Models

Template Credit: Adapted from a template made available by Dr. Jason Brownlee of Machine Learning Mastery.

Data Set Description: https://archive.ics.uci.edu/ml/datasets/Breast+Cancer+Wisconsin+(Original)

Benchmark References: https://www.kaggle.com/uciml/breast-cancer-wisconsin-data

Modeling Approach: binary classification, converting categorical to numerical attributes

Working through machine learning problems from end-to-end requires a structured modeling approach. Working problems through a project template can encourage you to think about the problem more critically, to challenge your assumptions, and to get good at all parts of a modeling project.

We will compare several different algorithms and determine which one would yield the best results. The project aims to touch on the following areas:

  1. Document a classification predictive modeling problem end-to-end.
  2. Explore data transformation options for improving model performance
  3. Explore algorithm tuning techniques for improving model performance

For this “Take-2” version of the project, we added the ensemble models to the exploration.

  1. Explore using and tuning ensemble methods for improving model performance

The HTML formatted report can be found here on GitHub.