Multiclass classification problems are those where a label must be predicted, but there are more than two labels that may be predicted.<\/p>\n
These are challenging predictive modeling problems because a sufficiently representative number of examples of each class is required for a model to learn the problem. It is made challenging when the number of examples in each class is imbalanced, or skewed toward one or a few of the classes with very few examples of other classes.<\/p>\n
Problems of this type are referred to as imbalanced multiclass classification problems and they require both the careful design of an evaluation metric and test harness and choice of machine learning models. The E.coli protein localization sites dataset is a standard dataset for exploring the challenge of imbalanced multiclass classification.<\/p>\n
In this tutorial, you will discover how to develop and evaluate a model for the imbalanced multiclass E.coli dataset.<\/p>\n
After completing this tutorial, you will know:<\/p>\n
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- 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 the class labels for specific examples.<\/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
![\"Imbalanced](\"https:\/\/machinelearningmastery.com\/wp-content\/uploads\/2020\/03\/Imbalanced-Multiclass-Classification-with-the-E.coli-Dataset.jpg\")
<\/p>\nImbalanced Multiclass Classification with the E.coli Dataset
Photo by Marcus<\/a>, some rights reserved.<\/p>\n<\/div>\nTutorial Overview<\/h2>\n
This tutorial is divided into five parts; they are:<\/p>\n
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- E.coli Dataset<\/li>\n
- Explore the Dataset<\/li>\n
- Model Test and Baseline Result<\/li>\n
- Evaluate Models\n
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- Evaluate Machine Learning Algorithms<\/li>\n
- Evaluate Data Oversampling<\/li>\n<\/ol>\n<\/li>\n
- Make Predictions on New Data<\/li>\n<\/ol>\n
E.coli Dataset<\/h2>\n
In this project, we will use a standard imbalanced machine learning dataset referred to as the “E.coli<\/em>” dataset, also referred to as the “protein localization sites<\/em>” dataset.<\/p>\n
The dataset describes the problem of classifying E.coli proteins<\/a> using their amino acid sequences in their cell localization sites. That is, predicting how a protein will bind to a cell based on the chemical composition of the protein before it is folded.<\/p>\n
The dataset is credited to Kenta Nakai and was developed into its current form by Paul Horton<\/a> and Kenta Nakai<\/a> in their 1996 paper titled “A Probabilistic Classification System For Predicting The Cellular Localization Sites Of Proteins<\/a>.” In it, they achieved a classification accuracy of 81 percent.<\/p>\n
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336 E.coli proteins were classified into 8 classes with an accuracy of 81% …<\/p>\n<\/blockquote>\n