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 glass identification 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 glass identification 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 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
![\"Evaluate](\"https:\/\/machinelearningmastery.com\/wp-content\/uploads\/2020\/03\/Evaluate-Models-for-the-Imbalanced-Multiclass-Glass-Identification-Dataset.jpg\")
<\/p>\nEvaluate Models for the Imbalanced Multiclass Glass Identification Dataset
Photo by Sarah Nichols<\/a>, some rights reserved.<\/p>\n<\/div>\nTutorial Overview<\/h2>\n
This tutorial is divided into five parts; they are:<\/p>\n
- \n
- Glass Identification Dataset<\/li>\n
- Explore the Dataset<\/li>\n
- Model Test and Baseline Result<\/li>\n
- Evaluate Models<\/li>\n
- Make Predictions on New Data<\/li>\n<\/ol>\n
Glass Identification Dataset<\/h2>\n
In this project, we will use a standard imbalanced machine learning dataset referred to as the “Glass Identification<\/em>” dataset, or simply “glass<\/em>.”<\/p>\n
The dataset describes the chemical properties of glass and involves classifying samples of glass using their chemical properties as one of six classes. The dataset was credited to Vina Spiehler<\/a> in 1987.<\/p>\n
Ignoring the sample identification number, there are nine input variables that summarize the properties of the glass dataset; they are:<\/p>\n
- \n
- RI<\/strong>: refractive index<\/li>\n
- Na<\/strong>: Sodium<\/li>\n
- Mg<\/strong>: Magnesium<\/li>\n
- Al<\/strong>: Aluminum<\/li>\n
- Si<\/strong>: Silicon<\/li>\n
- K<\/strong>: Potassium<\/li>\n
- Ca<\/strong>: Calcium<\/li>\n
- Ba<\/strong>: Barium<\/li>\n
- Fe<\/strong>: Iron<\/li>\n<\/ul>\n
The chemical compositions are measured as the weight percent in corresponding oxide.<\/p>\n
There are seven types of glass listed; they are:<\/p>\n
- \n
- Class 1<\/strong>: building windows (float processed)<\/li>\n
- Class 2<\/strong>: building windows (non-float processed)<\/li>\n
- Class 3<\/strong>: vehicle windows (float processed)<\/li>\n
- Class 4<\/strong>: vehicle windows (non-float processed)<\/li>\n
- Class 5<\/strong>: containers<\/li>\n
- Class 6<\/strong>: tableware<\/li>\n
- Class 7<\/strong>: headlamps<\/li>\n<\/ul>\n
- Class 2<\/strong>: building windows (non-float processed)<\/li>\n
- Na<\/strong>: Sodium<\/li>\n
- RI<\/strong>: refractive index<\/li>\n