In machine learning, multiclass or multinomial classification is the problem of classifying instances into one of three or more classes (classifying instances into one of two classes is called binary classification).

While many classification algorithms (notably multinomial logistic regression) naturally permit the use of more than two classes, some are by nature binary algorithms; these can, however, be turned into multinomial classifiers by a variety of strategies.

Multiclass classification should not be confused with multi-label classification, where multiple labels are to be predicted for each instance.

1. Perceptron
2. Naive Bayes
3. Decision Tree
4. Logistic Regression
5. K-Nearest Neighbor
6. Artificial Neural Networks/Deep Learning
7. Support Vector Machine

1. Multiclass Classification: A classification task with more than two classes; e.g., classify a set of images of fruits which may be oranges, apples, or pears. Multi-class classification makes the assumption that each sample is assigned to one and only one label: a fruit can be either an apple or a pear but not both at the same time.
2. Imbalanced Dataset: Imbalanced data typically refers to a problem with classification problems where the classes are not represented equally. For example, you may have a 3-class classification problem of set of fruits to classify as oranges, apples or pears with total 100 instances . A total of 80 instances are labeled with Class-1 (Oranges), 10 instances with Class-2 (Apples) and the remaining 10 instances are labeled with Class-3 (Pears). This is an imbalanced dataset and the ratio of 8:1:1. Most classification data sets do not have exactly equal number of instances in each class, but a small difference often does not matter. There are problems where a class imbalance is not just common, it is expected. For example, in datasets like those that characterize fraudulent transactions are imbalanced. The vast majority of the transactions will be in the “Not-Fraud” class and a very small minority will be in the “Fraud” class.