hands-on lab
Evaluating Binary Classification Models
Difficulty: Intermediate
Duration: Up to 1 hour
Students: 540
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