Multiclass classification in machine learning refers to an algorithm that can classify data into one of three or more classes. The goal of this type of model is to appropriately identify which class a new data point will fall into.
Binary classification is more limited and is used where a data point can only be categorized into one class or another, such as if you are labeling something as either positive or negative.
Multiclass classification, on the other hand, allows businesses to work with more complex datasets that include a wide variety of labels.
The multiclass classification algorithm relies on the assumption that a data point can only be assigned to one category. For instance, an animal can either be a dog or a cat, but not both. In mathematical terms, each data point belongs to one of N different categories.
The goal of a classification algorithm is to predict which category a data point belongs to and is one of the most common machine learning tasks that organizations employ to drive strategic decisions.
Many practical business problems can be solved by AI platforms that utilize multiclass classification algorithms. These machine learning models allow companies to gain valuable insights into their customer base. They will enable them to use the data they have gathered to gain a competitive advantage in their industry.
Consider, for example, how helpful it is for a business to be able to accurately predict what a customer is likely to purchase next based on recognized patterns and trends. Not only could they use this information to recommend products that customers will like, but it also will give them insight as to estimated revenues.
Multiclass classification models also help businesses determine feature impact – an important metric that identifies which features, or inputs, in the data have the most significant effect on the outputs of the model.
At LogicPlum, we recognize how beneficial a multiclass classification algorithm can be for your organization. We take the guesswork out of developing this, and our platform automatically identifies which classification algorithm will be best for your target variable.
Our tools include an analysis of feature impact, so your business can be armed with knowledge about what is driving the outputs of the machine learning models at your disposal. We also provide a confusion matrix, so you can analyze how accurate each model is for your given inputs.