Klasszifikátors

A klasszifikátor, often anglicized as classifier, is a fundamental concept in machine learning and statistics. It refers to an algorithm or function that assigns an input data point to one of several predefined categories or classes. The goal of a classifier is to learn a boundary or decision rule that separates data points belonging to different classes. These classes are typically mutually exclusive, meaning a data point can only belong to one class.

The process of building a classifier involves training a model on a dataset of labeled examples. Each example consists of a set of features describing the data point and its corresponding correct class. The classifier learns patterns and relationships between the features and their associated classes from this training data. Once trained, the classifier can then be used to predict the class of new, unseen data points based on their features.

Common examples of classification tasks include spam detection (classifying emails as spam or not spam), image recognition (identifying objects in images), and medical diagnosis (predicting the presence or absence of a disease). Various algorithms can be employed as classifiers, such as logistic regression, support vector machines, decision trees, and neural networks, each with its own strengths and weaknesses depending on the specific problem and data characteristics. The performance of a classifier is typically evaluated using metrics like accuracy, precision, recall, and F1-score.