University of Twente Student Theses


A sensitivity analysis of different machine learning methods

Frolov, Platon (2021) A sensitivity analysis of different machine learning methods.

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Abstract:Datasets often contain noisy data due to faulty calibration of sensors or human error. A substantial amount of research has been conducted on the impact of noise on the accuracy of models to provide explainability of these so-called black-box models. However, less research has been conducted on how the noise impacts the precision of the models, which could provide an additional dimension of explainability about the robustness of the models. This paper provides insight into the robustness and explainability of machine learning regression methods by looking at what the influence of perturbations in numerical features in training data is on the variance of the output of linear regression, regression trees and multi-layer perceptron regression methods. The research has been conducted with an experimental approach in which the regression methods were exposed to different variances in Gaussian noise added to attributes in the training dataset. From the experiments, it appeared that decision trees are notably more sensitive to attribute noise than linear regression, and multi-layer perceptron regression. The latter two methods show a high tolerance to noise in the training data on the specific datasets.
Item Type:Essay (Bachelor)
Faculty:EEMCS: Electrical Engineering, Mathematics and Computer Science
Subject:54 computer science
Programme:Computer Science BSc (56964)
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