CONSTRUCTION OF A MODEL BASED ON THE RANDOM DRILL - Mubakilayi, Yannick; Kambaja, Jean; Mbuyi, Ornella
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This work is part of the statistical learning of data for model development. More specifically, it is devoted to the study of random forests which are one of the most recent algorithms in the family of pattern classification methods. A great advantage of random forest is that it can be used for both classification and regression problems, which constitute the majority of current machine learning systems. In this work, we used coronavirus data to generate and evaluate the proposed model. We chose random forests, which apply uniform majority voting of decision trees to produce an optimal…mehr

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Produktbeschreibung
This work is part of the statistical learning of data for model development. More specifically, it is devoted to the study of random forests which are one of the most recent algorithms in the family of pattern classification methods. A great advantage of random forest is that it can be used for both classification and regression problems, which constitute the majority of current machine learning systems. In this work, we used coronavirus data to generate and evaluate the proposed model. We chose random forests, which apply uniform majority voting of decision trees to produce an optimal prediction, to classify patients who tested positive or negative for coronavirus. The data were used as a training sample to design a decision model.
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Autorenporträt
Informático. Desde el punto de vista académico, tiene un máster en Ciencias Matemáticas por el Instituto Africano de Ciencias Matemáticas de Kigali (Ruanda) y una licenciatura en Ingeniería Informática por la Universidad de Mbujimayi. Actualmente enseña en la Universidad de Mbujimayi, la Universidad Oficial de Mbujimayi, la Universidad Franco-Americana,...