Abstract

Classification is a major tool of statistics and machine learning. A classification method first processes a training set of objects with given classes (labels), with the goal of afterward assigning new objects to one of these classes. When running the resulting prediction method on the training data or on test data, it can happen that an object is predicted to lie in a class that differs from its given label. This is sometimes called label bias, and raises the question whether the object was mislabeled. The proposed class map reflects the probability that an object belongs to an alternative class, how far it is from the other objects in its given class, and whether some objects lie far from all classes. The goal is to visualize aspects of the classification results to obtain insight in the data. The display is constructed for discriminant analysis, the k-nearest neighbor classifier, support vector machines, logistic regression, and coupling pairwise classifications. It is illustrated on several benchmark datasets, including some about images and texts.

摘要

分类是统计和机器学习的主要工具。分类方法首先处理具有给定类（标签）的训练对象集，目标是随后将新对象分配给这些类中的一个。在训练数据或测试数据上运行生成的预测方法时，可能会预测对象位于与其给定标签不同的类别中。这有时被称为标签偏差，并提出了对象是否被错误标记的问题。提议的类图反映了一个对象属于另一个类的概率，它与给定类中的其他对象的距离，以及某些对象是否远离所有类。目标是可视化分类结果的各个方面，以深入了解数据。该显示是为判别分析、k-最近邻分类器、支持向量机、逻辑回归和耦合成对分类而构建的。它在几个基准数据集上进行了说明，包括一些关于图像和文本的数据。