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Chapter 11. Classification Modelsстатья
- Авторы: Baskin I.I., Marcou G., Horvath D., Varnek A.
- Сборник: Tutorials in Chemoinformatics. First Edition. Edited by Alexandre Varnek
- Глава в коллективной монографии
- Год издания: 2017
- Место издания: John Wiley & Sons Ltd United Kingdom
- Первая страница: 175
- Последняя страница: 192
- DOI: 10.1002/9781119161110.ch11
- Аннотация: This chapter illustrates the use of three classification methods and introduces measures of success for classification. The first one, the Naïve Bayes algorithm, focuses on a statistical description of the data. The second one, the Support Vector Machine, provides a geometric view of the classification problem. The third one, the k-Nearest Neighbor algorithm relies on the assumption that similar objects should have similar properties. A very common measure of success for solving classification problems is the accuracy: the rate of successful classification. The statistical performances assessed by the different measures of success allow comparing the models on the test set. An external test set is available to test the models in this case. Yet, it is very useful to have a closer look at the errors of the models, especially during the fit or when combining the models.
- Добавил в систему: Баскин Игорь Иосифович