Please use this identifier to cite or link to this item:
Title: Classification problems in machine learning
Authors: CHENG, SHENG QI(程勝祺)
Department: Department of Mathematics
Faculty: Faculty of Science and Technology
Issue Date: 2021
Citation: Cheng, S. Q. (2021). Classification problems in machine learning (OAPS)). Retrieved from University of Macau, Outstanding Academic Papers by Students Repository.
Abstract: In this report, we will introduce and study some different classifiers of scikit-learn which is machine learning in Python. First, we apply 3 different classifications (SGD classifier, random forest classifier and k-nearest neighbors classifier) on a model of identifying a digit from 70,000 handwritten images and compare their advantages and disadvantages. Then we make the combination of the other 3 classifications(logistic regression classifier, decision tree classifier and k-nearest neighbors classifier) as a new ensemble classifier by the majority voting principle. We evaluate and tune the new ensemble classifier through the decision area. And the regularization parameter C of logistic regression classifier and the depth of decision tree are tuned by grid search.
Course: Bachelor of Science in Mathematics
Instructor: Dr. Xu Lihu
Programme: Bachelor of Science in Mathematics
Appears in Collections:FST OAPS 2021

Files in This Item:
File Description SizeFormat 
OAPS_2021_FST_DB726619_Cheng ShengQi_Classification problems in machine learning.pdf14.3 MBAdobe PDFView/Open

Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.