  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.
