Moniluokittelutehtävissä

Moniluokittelutehtävissä, or multi-class classification in English, is a fundamental problem in machine learning and pattern recognition. It involves assigning an input instance to one of three or more distinct categories or classes. This contrasts with binary classification, where the task is to choose between only two possible outcomes.

The goal of a multi-class classification model is to learn a mapping from input features to the correct class label. For example, classifying images of animals could involve categories like 'cat', 'dog', 'bird', and 'fish'. Given an image, the model must decide which of these four classes it belongs to.

Common algorithms used for multi-class classification include extensions of binary classifiers, such as one-vs-rest (OvR) or one-vs-one (OvO) strategies applied to algorithms like Support Vector Machines (SVMs) or logistic regression. Alternatively, some algorithms are inherently designed for multi-class problems, such as multinomial logistic regression, decision trees, and neural networks with a softmax activation function in the output layer.

The performance of a multi-class classifier is typically evaluated using metrics like accuracy, precision, recall, F1-score, and confusion matrices, often calculated on a per-class basis or as an average across all classes. Choosing the appropriate algorithm and evaluation metrics depends heavily on the specific dataset and the problem's requirements.