Supervised Learning: Classification
| Autor: | Leonardo Vanneschi, Álvaro Rubio Largo, Mauro Castelli |
|---|---|
| Rok vydání: | 2019 |
| Předmět: |
Computer science
business.industry Supervised learning Semi-supervised learning Machine learning computer.software_genre Class (biology) Task (project management) Multiclass classification ComputingMethodologies_PATTERNRECOGNITION LPBoost Unsupervised learning Artificial intelligence Set (psychology) business computer |
| Zdroj: | Encyclopedia of Bioinformatics and Computational Biology (1) |
| DOI: | 10.1016/b978-0-12-809633-8.20332-4 |
| Popis: | Supervised learning is the task of building a model that is able to fit the available observations. In the area of supervised learning, classification is one of the most studied problems. Given a set of predefined class labels (two or more) and a set of available observations, the aim is to build a model based on the features of the observations that is able to assign each observation to the corresponding class. In Bioinformatics several problems can be formulated as a classification task. This article introduces several supervised learning techniques that are commonly used to address a classification problem, presenting the most used measures to evaluate the performance of a classification model. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
|