mar-ina-thal/nested-cv

Use the nested cross validation method in order to fine tune and test the perfomance of 5 machine learning algorithms, namely Sup- port Vector Machines, Linear Regression, Gaussian Naive Bayes and Linear Discriminant Analysis, on the Hepatitis C dataset

This repository contains the coursework for the Data Science & Information Technologies masters course: Machine Learning in Computational Biology at the National and Kapodistrian University of Athens (NKUA) during the Spring 2023 semester

Abstract

In this paper we use the nested cross validation method in order to fine tune and test the perfomance of 5 machine learning algorithms, namely Support Vector Machines, Linear Regression, Gaussian Naive Bayes and Linear Discriminant Analysis, on the Hepatitis C dataset. After fine tuning the algorithms we select the one with the best perfomance on the dataset and we evaluate several perfomance metrics on the dataset.

Dataset description

The Hepatitis C dataset is an openly available dataset which consist of 14 attributes

Contents

1. Project Report
2. Nested-cv python notebook