Hyperparameter optimization on supervised learning models

Lytra, Georgia; Λύτρα, Γεωργία

dc.contributor.advisor	Kyriazis, Dimosthenis
dc.contributor.advisor	Κυριαζής, Δημοσθένης
dc.contributor.author	Lytra, Georgia
dc.contributor.author	Λύτρα, Γεωργία
dc.date.accessioned	2021-05-25T08:24:51Z
dc.date.available	2021-05-25T08:24:51Z
dc.date.issued	2021
dc.identifier.uri	https://dione.lib.unipi.gr/xmlui/handle/unipi/13457
dc.identifier.uri	http://dx.doi.org/10.26267/unipi_dione/880
dc.format.extent	56	el
dc.language.iso	en	el
dc.publisher	Πανεπιστήμιο Πειραιώς	el
dc.rights	Αναφορά Δημιουργού-Μη Εμπορική Χρήση-Όχι Παράγωγα Έργα 3.0 Ελλάδα	*
dc.rights	Αναφορά Δημιουργού-Μη Εμπορική Χρήση-Όχι Παράγωγα Έργα 3.0 Ελλάδα	*
dc.rights.uri	http://creativecommons.org/licenses/by-nc-nd/3.0/gr/	*
dc.title	Hyperparameter optimization on supervised learning models	el
dc.type	Master Thesis	el
dc.contributor.department	Σχολή Τεχνολογιών Πληροφορικής και Επικοινωνιών. Τμήμα Ψηφιακών Συστημάτων	el
dc.description.abstractEN	Machine learning has taken the technological world by storm in recent years. Every practitioner needs to develop a model that meets the needs of the problem that is faced along with all the available resources that come by. A lot of challenges come up along the way of this process; one of these challenges is the selection of the most appropriate hyperparameters in the developing model. This phase, called hyperparameter optimization, is crucial since on the one hand there are models that have proven to be effective in both performance and execution time, while on the other hand these same models can be rendered rather useless without the appropriate selection of hyperparameters. In addition, hyperparameter tuning can really help a model to shine and exploit its capabilities to the fullest. Since every problem is unique and complex, domain knowledge is required to select the appropriate hyperparameters in each case; but that is not always possible. A need is on the rise for tools that automatically solve this issue and give information and guidance to the users on how to solve the problem at hand. This thesis follows an experimental procedure to extract information regarding the appropriate hyperparameters on various supervised learning models. We use datasets with diverse features and characteristics that could assist with the automation of machine learning processes. This is approached through already existing optimization frameworks that have been proven to achieve great results on hyperparameter tuning.	el
dc.contributor.master	Πληροφοριακά Συστήματα και Υπηρεσίες	el
dc.subject.keyword	Hyperparameters	el
dc.subject.keyword	Hyperparameter optimization	el
dc.subject.keyword	Hyperparameter tuning	el
dc.subject.keyword	Supervised learning	el
dc.subject.keyword	Hyperopt	el
dc.subject.keyword	Scikit-learn	el
dc.subject.keyword	Bayesian optimization	el
dc.subject.keyword	Tree-structured parzen estimator	el
dc.subject.keyword	Classification	el
dc.subject.keyword	Regression	el
dc.subject.keyword	Machine learning	el
dc.date.defense	2021-02-26

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Αναφορά Δημιουργού-Μη Εμπορική Χρήση-Όχι Παράγωγα Έργα 3.0 Ελλάδα