Misclassification errors on the minority class are more important than other types of prediction errors for some imbalanced classification tasks.<\/p>\n
One example is the problem of classifying bank customers as to whether they should receive a loan or not. Giving a loan to a bad customer marked as a good customer results in a greater cost to the bank than denying a loan to a good customer marked as a bad customer.<\/p>\n
This requires careful selection of a performance metric that both promotes minimizing misclassification errors in general, and favors minimizing one type of misclassification error over another.<\/p>\n
The German credit dataset<\/strong> is a standard imbalanced classification dataset that has this property of differing costs to misclassification errors. Models evaluated on this dataset can be evaluated using the Fbeta-Measure<\/a> that provides a way of both quantifying model performance generally, and captures the requirement that one type of misclassification error is more costly than another.<\/p>\n In this tutorial, you will discover how to develop and evaluate a model for the imbalanced German credit classification dataset.<\/p>\n After completing this tutorial, you will know:<\/p>\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 an Imbalanced Classification Model to Predict Good and Bad Credit This tutorial is divided into five parts; they are:<\/p>\n In this project, we will use a standard imbalanced machine learning dataset referred to as the “German Credit<\/a>” dataset or simply “German<\/em>.”<\/p>\n The dataset was used as part of the Statlog project, a European-based initiative in the 1990s to evaluate and compare a large number (at the time) of machine learning algorithms on a range of different classification tasks. The dataset is credited to Hans Hofmann.<\/p>\n The fragmentation amongst different disciplines has almost certainly hindered communication and progress. The StatLog project was designed to break down these divisions by selecting classification procedures regardless of historical pedigree, testing them on large-scale and commercially important problems, and hence to determine to what extent the various techniques met the needs of industry.<\/p>\n<\/blockquote>\n\n
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Photo by AL Nieves<\/a>, some rights reserved.<\/p>\n<\/div>\nTutorial Overview<\/h2>\n
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German Credit Dataset<\/h2>\n
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