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Binary classification is a kind of machine learning that predicts whether an item belongs to one of two classes. Example applications of binary classification are predicting if an email is spam or not, if a user will buy a new product, or determining if a tissue sample is benign or malignant. In this Lab, you will learn about binary classification and model evaluation in AWS as you diagnose cancer with an Amazon Machine Learning binary classifier. You will train a model with medical data, evaluate the model's performance, and use the model to make diagnoses.
Upon completion of this Lab you will be able to:
- Create Amazon Machine Learning datasources, and binary classification models
- Understand model how recipes can be used to transform data
- Interpret model evaluation results and how model parameters impact them
- Perform real-time predictions with the model
You should be familiar with:
- Basic Amazon S3 concepts
Before completing the Lab instructions, the environment will look as follows:
After completing the Lab instructions, the environment should look similar to:
January 10th, 2019 - Added a validation Lab Step to check the work you perform in the Lab
About the Author
Logan has been involved in software development and research since 2007 and has been in the cloud since 2012. He is an AWS Certified DevOps Engineer - Professional, AWS Certified Solutions Architect - Professional, MCSE: Cloud Platform and Infrastructure, Google Cloud Certified Associate Cloud Engineer, Certified Kubernetes Administrator (CKA), Certified Kubernetes Application Developer (CKAD), Linux Foundation Certified System Administrator (LFCS), and Certified OpenStack Administrator (COA). He earned his Ph.D. studying design automation and enjoys all things tech.