Description: Multiclass refers to classification tasks where there are more than two classes to predict. In the context of machine learning, this approach is fundamental for solving problems where data can be categorized into multiple groups. Unlike binary classification, which only has two possible outcomes, multiclass classification allows a model to assign an input to one of several possible categories. This is achieved through the use of algorithms that can handle multiple outputs, such as multinomial logistic regression, support vector machines (SVM), and neural networks. In machine learning frameworks, activation functions like softmax are commonly used in the output layer to normalize the model’s outputs, ensuring that the sum of the probabilities of all classes equals one. This approach is essential in applications where decisions must be made among several options, such as in image recognition, where a model can identify different objects in an image. The ability to handle multiple classes not only increases the complexity of the model but also requires a well-labeled and balanced dataset to ensure optimal performance.
Uses: Multiclass classification is used in various applications, such as image recognition, where a model can identify different objects in an image, and in natural language processing, where texts can be classified into multiple categories, such as topics or sentiments. It is also applied in recommendation systems, where products or services can be suggested to users based on multiple features.
Examples: An example of multiclass classification is the handwritten digit recognition model, such as the MNIST dataset, where each image of a digit can be classified into one of ten categories (0-9). Another example is classifying emails into categories such as ‘spam’, ‘promotions’, and ‘important’.
