Understanding of Algorithms. KNNs and Naive Bayes

Project Report from the year 2021 in the subject Computer Science - Applied, grade: 17/20, University of Poitiers, course: Machine Learning, language: English, abstract: In this project, we would tackle three different parts using Python programming language and JupyterLab. The first part is focusing on programming KNNs (K-nearest neighbors) and NBs (Naive Bayes) from scratch. Then, we would move on afterward to comparing the results obtained by these both algorithms for final evaluation. Therefore, we would consider which one is performing the best. In the second part, we would use sklearn library to compare the two algorithms on a larger dataset, specifically in four different settings: Influence of reduced training set, influence of large training set, influence of absence of a teacher and unknown distribution. In the third part, we would compare the same algorithms for image classification on 10 different classes, using feature descriptors.