hands-on lab

Evaluating Binary Classification Models

Up to 1h
Get guided in a real environmentPractice with a step-by-step scenario in a real, provisioned environment.
Learn and validateUse validations to check your solutions every step of the way.
See resultsTrack your knowledge and monitor your progress.


How do you know if the model you have built is a good predictor of your output variable?
This lab will walk you through building several binary classification models using different model methodologies and then comparing the model predictions using evaluation tools such as accuracy, a confusion matrix or an ROC curve.

Learning Objectives

Upon completion of this lab you will be able to:

  • Import data using pandas
  • Prepare data for modeling
  • Build classification models using scikit-learn
  • Evaluate the classification models using accuracy score, confusion matrix and ROC curve metrics

Intended Audience

This lab is intended for:

  • Machine learning engineers
  • Anyone interested in evaluating machine learning model performance


You should possess:

  • A basic understanding of Python

About the author

Learning paths

Calculated Systems was founded by experts in Hadoop, Google Cloud and AWS. Calculated Systems enables code-free capture, mapping and transformation of data in the cloud based on Apache NiFi, an open source project originally developed within the NSA. Calculated Systems accelerates time to market for new innovations while maintaining data integrity.  With cloud automation tools, deep industry expertise, and experience productionalizing workloads development cycles are cut down to a fraction of their normal time. The ability to quickly develop large scale data ingestion and processing  decreases the risk companies face in long development cycles. Calculated Systems is one of the industry leaders in Big Data transformation and education of these complex technologies.

Covered topics

Lab steps

Logging In to the Amazon Web Services Console
Opening the Lab's Jupyter Notebook
Solutions to Evaluating Binary Classification Models