Mehrklassen Klassifikation is a type of supervised maschinellem Lernen task where the objective is to classify instances into one of three or more classes. Unlike binärer Klassifikation, which deals with two classes, multi-class classification presents a more complex Herausforderung, da sie darin besteht, zwischen mehreren Kategorien zu unterscheiden.
This process typically involves training a model on a labeled dataset, where each instance is associated with a specific class label. Common algorithms used for multi-class classification include decision trees, Support-Vektor-Maschinen, and neuronale Netze, particularly in the context of deep learning.
Bewertungsmetriken for multi-class classification often include accuracy, precision, recall, and F1-score, which provide insights into the model’s performance across all classes. Additionally, confusion matrices are frequently employed to visualize the classification results and understand how well the model performs for each class.
Multi-class classification has a wide range of applications, from image recognition and der Verarbeitung natürlicher Sprache to medical diagnosis and more. For instance, in image recognition, a model might classify images into categories such as ‘cat’, ‘dog’, or ‘bird’. In natural language processing, it can be used to categorize text into topics or sentiments.