Evaluating Binary Classification Models

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

Updates

January 15th, 2025 - Removed deprecated methods and updated instructions