The Guide to machine learning using cancer data notebook explains how to use machine learning for a classification problem on tumor data. You’ll see how to develop a solution in three parts, starting with an intuitive introduction to supervised learning concepts, followed by a basic example of a machine learning model. The final section is a deep dive into model stacking and parameter tuning, both of which are used in practice to significantly improve predictive accuracy.

If you are interested in running this notebook in your own Watson Studio environment, you can get the notebook from GitHub.

Join The Discussion

Your email address will not be published. Required fields are marked *