multiclassification

Multiclassification, also known as multi-class classification, is a type of supervised machine learning task where an instance is assigned to one of three or more distinct classes. This is in contrast to binary classification, where instances are assigned to one of only two classes. In multiclassification, the set of possible output labels is mutually exclusive. For example, classifying an image as either a cat, dog, or bird is a multiclassification problem. The decision tree algorithm is a common approach for multiclassification. Other methods can be adapted from binary classification.

One common strategy to adapt binary classifiers for multiclass problems is the one-vs-rest (OvR) approach. In OvR, a separate binary classifier is trained for each class. Each classifier distinguishes between its assigned class and all other classes combined. During prediction, the instance is passed through all classifiers, and the class corresponding to the classifier that outputs the highest confidence score is chosen. Another common approach is the one-vs-one (OvO) method. Here, a binary classifier is trained for every possible pair of classes. For an N-class problem, N*(N-1)/2 classifiers are trained. During prediction, an instance is classified by all pairwise classifiers, and the class that wins the most pairwise comparisons is selected.

Multiclassification problems are prevalent in various domains, including natural language processing (e.g., sentiment analysis with categories like positive, negative, neutral), image recognition (e.g., identifying different types of objects in an image), and spam detection (where emails can be categorized into multiple spam types beyond just spam/not-spam). The choice of algorithm and adaptation strategy often depends on the specific dataset and performance requirements.