klassifikationsmodell

A klassifikationsmodell, or classification model, is a type of machine learning algorithm used to assign data points to predefined categories or classes. The goal of a classification model is to learn a mapping from input features to these discrete output labels. This is in contrast to regression models, which predict continuous numerical values.

Classification models are trained on a dataset containing labeled examples, where each example has a set of features and a corresponding known class. The model learns patterns and relationships within this data to generalize and predict the class of new, unseen data. Common applications include spam detection (classifying emails as spam or not spam), image recognition (identifying objects in images), and medical diagnosis (predicting the presence of a disease).

There are numerous algorithms used for classification, each with its own strengths and weaknesses. Some popular examples include logistic regression, support vector machines (SVMs), decision trees, random forests, and neural networks. The choice of algorithm often depends on the nature of the data, the complexity of the problem, and the desired performance.

The performance of a classification model is typically evaluated using metrics such as accuracy, precision, recall, and F1-score. These metrics help assess how well the model correctly identifies the true classes and distinguishes between them. Overfitting, where a model performs well on training data but poorly on new data, is a common challenge that needs to be addressed through techniques like cross-validation and regularization.