XGBoost Design and Configuration with Hyperparameter Tuning.

XGBoost Design and Configuration with Hyperparameter Tuning.

<p>XGBoost Design and Configuration with Hyperparameter Tuning.</p>

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Bibliographic Details
Main Author:	Rume Elizabeth Yoro (21608534) (author)
Other Authors:	Margaret Dumebi Okpor (21608537) (author), Maureen Ifeanyi Akazue (21608540) (author), Ejaita Abugor Okpako (21608543) (author), Andrew Okonji Eboka (21608546) (author), Patrick Ogholuwarami Ejeh (21608549) (author), Arnold Adimabua Ojugo (21608552) (author), Chris Chukwufunaya Odiakaose (21608555) (author), Amaka Patience Binitie (21608558) (author), Rita Erhovwo Ako (21608561) (author), Victor Ochuko Geteloma (21608564) (author), Paul Avwerosuo Onoma (21608567) (author), Asuobite ThankGod Max-Egba (21608570) (author), Ayei Egu Ibor (21608573) (author), Sunny Innocent Onyemenem (21608576) (author), Elochukwu Ukwandu (21608579) (author)
Published:	2025
Subjects:	Sociology Science Policy Biological Sciences not elsewhere classified Information Systems not elsewhere classified vanishing gradient problem use deep learning transfer learning scheme proposed ensemble demonstrates prevent resource exploits often degraded due machine learning approach intrusion detection systems exploit unsuspecting users effectively classify 314 aid effective identification using advanced tools ensemble yields accuracy compromise network resources traditional ml performance learner </ p yields solution network security inspired tools xlink "> xgboost meta simplistic design service disruption security experts schemes based recurrent networks random forest penetrative techniques monetization efficacy local maxima enhanced productivity defined sip defensive measures constant evolution competitive marketing climbing mode boosted meta also evolve
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