Multinomial classification

In ML, multinomial (also known as multiclass) classification is the task of classifying data objects or instances into more than two classes, that is, having more than two labels or classes. Classifying data objects or instances into two classes is called binary classification. More technically, in multinomial classification, each training instance belongs to one of N different classes subject to N >=2. The goal is then to construct a model that correctly predicts the classes to which the new instances belong. There may be numerous scenarios having multiple categories in which the data points belong. However, if a given point belongs to multiple categories, this problem decomposes trivially into a set of unlinked binary problems, which can be solved naturally using a binary classification algorithm.

Readers are suggested not be confused distinguishing the multiclass classification with multilabel classification, where multiple labels are to be predicted for each instance. For more on Spark-based implementation for the multilabel classification, interested readers should refer to https://spark.apache.org/docs/latest/mllib-evaluation-metrics.html#multilabel-classification.

Multiclass classification techniques can be divided into several categories as follows:

Transformation to binary
Extension from binary
Hierarchical classification

Table of Contents for Multinomial classification

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Table of Contents for
Multinomial classification