Multiclass Classification vs. Multilabel Classification

Attribute	Multiclass Classification	Multilabel Classification
Number of classes	More than two classes	Can have multiple labels per instance
Output	Single class label per instance	Multiple class labels per instance
Model complexity	Usually simpler models	May require more complex models
Evaluation metrics	Accuracy, F1 score, etc.	Hamming loss, subset accuracy, etc.

Introduction

When it comes to machine learning tasks, classification is a common problem that involves assigning a label or category to a given input. Multiclass classification and multilabel classification are two variations of this task that have their own unique attributes and applications. In this article, we will compare the key differences between multiclass classification and multilabel classification.

Definition

Multiclass classification is a type of classification task where the goal is to classify instances into one of three or more classes. Each instance is assigned to one and only one class. On the other hand, multilabel classification is a type of classification task where each instance can be assigned multiple labels simultaneously. This means that an instance can belong to more than one class at the same time.

Label Assignment

In multiclass classification, each instance is assigned to one and only one class from a set of mutually exclusive classes. This means that the classes are distinct and do not overlap. For example, in a classification task to predict the type of fruit, each fruit can only belong to one category such as apple, banana, or orange. In contrast, multilabel classification allows for instances to be assigned multiple labels from a set of non-mutually exclusive classes. For example, in a classification task to predict the topics of a news article, an article can be labeled with multiple topics such as politics, sports, and entertainment.

Model Complexity

When it comes to model complexity, multiclass classification is generally simpler compared to multilabel classification. This is because in multiclass classification, the model only needs to predict one class for each instance. On the other hand, in multilabel classification, the model needs to predict multiple labels for each instance, which can increase the complexity of the task. This complexity can be further compounded when dealing with a large number of labels or when there is a high degree of label correlation.

Evaluation Metrics

When evaluating the performance of a multiclass classification model, common metrics such as accuracy, precision, recall, and F1 score are used. These metrics provide insights into how well the model is performing in terms of correctly classifying instances into their respective classes. In contrast, evaluating a multilabel classification model requires different metrics such as Hamming loss, precision at k, and recall at k. These metrics take into account the fact that each instance can have multiple labels and provide a more nuanced evaluation of the model's performance.

Training Data

In multiclass classification, the training data consists of instances with a single class label assigned to each instance. This means that each instance is associated with only one ground truth label. On the other hand, in multilabel classification, the training data consists of instances with multiple labels assigned to each instance. This means that each instance can have multiple ground truth labels, which can make the training process more challenging.

Applications

Multiclass classification is commonly used in tasks such as image classification, sentiment analysis, and speech recognition where instances belong to one of several distinct classes. On the other hand, multilabel classification is used in tasks such as document categorization, tag prediction, and recommendation systems where instances can belong to multiple categories simultaneously. The choice between multiclass and multilabel classification depends on the specific requirements of the task at hand.