hands-on lab

Evaluating Binary Classification Models

Difficulty: Intermediate
Duration: Up to 1 hour
Students: 452
Rating: 4.4/5
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.

Description

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

Prerequisites

You should possess:

  • A basic understanding of Python

Covered topics

Lab steps

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