Many binary classification tasks do not have an equal number of examples from each class, e.g. the class distribution is skewed or imbalanced.<\/p>\n
A popular example is the adult income dataset that involves predicting personal income levels as above or below $50,000 per year based on personal details such as relationship and education level. There are many more cases of incomes less than $50K than above $50K, although the skew is not severe.<\/p>\n
This means that techniques for imbalanced classification can be used whilst model performance can still be reported using classification accuracy, as is used with balanced classification problems.<\/p>\n
In this tutorial, you will discover how to develop and evaluate a model for the imbalanced adult income classification dataset.<\/p>\n
After completing this tutorial, you will know:<\/p>\n
- \n
- How to load and explore the dataset and generate ideas for data preparation and model selection.<\/li>\n
- How to systematically evaluate a suite of machine learning models with a robust test harness.<\/li>\n
- How to fit a final model and use it to predict class labels for specific cases.<\/li>\n<\/ul>\n
Discover SMOTE, one-class classification, cost-sensitive learning, threshold moving, and much more in my new book<\/a>, with 30 step-by-step tutorials and full Python source code.<\/p>\n
Let’s get started.<\/p>\n
![\"Develop](\"https:\/\/machinelearningmastery.com\/wp-content\/uploads\/2020\/03\/Develop-an-Imbalanced-Classification-Model-to-Predict-Income.jpg\")
<\/p>\nDevelop an Imbalanced Classification Model to Predict Income
Photo by Kirt Edblom<\/a>, some rights reserved.<\/p>\n<\/div>\nTutorial Overview<\/h2>\n
This tutorial is divided into five parts; they are:<\/p>\n
- \n
- Adult Income Dataset<\/li>\n
- Explore the Dataset<\/li>\n
- Model Test and Baseline Result<\/li>\n
- Evaluate Models<\/li>\n
- Make Prediction on New Data<\/li>\n<\/ol>\n
Adult Income Dataset<\/h2>\n
In this project, we will use a standard imbalanced machine learning dataset referred to as the “Adult Income<\/em>” or simply the “adult<\/em><\/a>” dataset.<\/p>\n
The dataset is credited to Ronny Kohavi and Barry Becker and was drawn from the 1994 United States Census Bureau<\/a> data and involves using personal details such as education level to predict whether an individual will earn more or less than $50,000 per year.<\/p>\n
\n
The Adult dataset is from the Census Bureau and the task is to predict whether a given adult makes more than $50,000 a year based attributes such as education, hours of work per week, etc..<\/p>\n<\/blockquote>\n