Details
Machine Learning Approaches for Predicting AIDS Virus Infection
1. Auflage
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CHF 6.00 |
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| Verlag: | Grin Verlag |
| Format: | |
| Veröffentl.: | 05.09.2024 |
| ISBN/EAN: | 9783389065747 |
| Sprache: | englisch |
| Anzahl Seiten: | 9 |
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Beschreibungen
Academic Paper from the year 2024 in the subject Computer Science - Bioinformatics, grade: 1.5, , course: Biotechnology, language: English, abstract: This review investigates the use of machine learning approaches, notably Random Forest and Neural Network classifiers, in the context of AIDS classification and digit identification using the MNIST dataset.
The paper compares the performance of a Random Forest classifier and a Multi-Layer Perceptron (MLP) neural network on an AIDS classification dataset, emphasizing the significance of feature scaling and the impact of model design on classification accuracy. The Random Forest model was used to determine feature relevance, and the MLP classifier was trained and tested for accuracy in categorizing the binary outcome of HIV infection.
The paper compares the performance of a Random Forest classifier and a Multi-Layer Perceptron (MLP) neural network on an AIDS classification dataset, emphasizing the significance of feature scaling and the impact of model design on classification accuracy. The Random Forest model was used to determine feature relevance, and the MLP classifier was trained and tested for accuracy in categorizing the binary outcome of HIV infection.
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