Work for Artificial Intelligence classes from Federal University of Espirito Santo (UFES)
using Machine Learning
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06/22 - 06/22
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The objective is to compare the accuracy of five supervised machine learning methods, namely ZeroR (ZR), Gaussian Naive Bayes (NBG), KMeans Centroids (KMC), K Nearest Neighbors (KNN), and Decision Tree (AD), in classifying the wine database from scikit-learn. I aim to present the results of this comparison through an accuracy table and boxplots, which will showcase the visual differences between the classifiers. Additionally, I will perform a statistical analysis by comparing each method pair-wise using both the paired t-test and the Wilcoxon test. The goal of this analysis is to establish whether there are any significant statistical differences between the methods.
