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http://hdl.handle.net/10609/132748
Title: | A systematic study of the performance of Feedforward Neural Networks on data from closed-form mathematical models |
Author: | Ríos Romero, Harry de los |
Tutor: | Duch, Jordi |
Abstract: | In the age of data, Neural Networks have proven to be powerful predictive tools. However, in scenarios where Model Interpretability prevails over accuracy, other statistical tools may be more appropriate. One such tool is the Bayesian Machine Scientist, a tool based on Bayesian Statistics and Statistical Physics that explores the space of closed-form mathematical models using Markov Chain Monte Carlo and finds the most plausible model given data. In this paper we have made a systematic study of the accuracy of FeedForward Neural Networks, the Bayesian Machine Scientist and a Random Forest algorithm, using the RMSE metric on two types of scenarios. The first one, on data generated by another FeedForward Neural Network, and the second one on a set of data with different noise levels, generated by means of three closed-form mathematical models coming from the Bayesian Machine Scientist. The results show that in the noise-free scenario the Neural Networks are unbeatable but in a real scenario with noise, its performance decreases and could be outperformed by the Bayesian Machine Scientist in the low noise range with the advantage that the latter offers much more interpretable models. |
Keywords: | bayesian machine FeedForward neural networks |
Document type: | info:eu-repo/semantics/masterThesis |
Issue Date: | Jun-2021 |
Publication license: | http://creativecommons.org/licenses/by-nc-nd/3.0/es/ |
Appears in Collections: | Bachelor thesis, research projects, etc. |
Files in This Item:
File | Description | Size | Format | |
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hde_los_riosTFM0621memory.pdf | Memory of TFM | 1,69 MB | Adobe PDF | View/Open |
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