[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-118531-en":3,"doc-seo-118531-105":30,"detail-sidebar-cat-0-en-105":83},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":4,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},118531,962075114101,"Seraphina","https://ap-avatar.wpscdn.com/avatar/e000253a75eb197efd?x-image-process=image/resize,m_fixed,w_180,h_180&k=1780044092746381165",8,"Research & Report","Breast Cancer Classification using different Machine Learning Algorithms - Thesis","Breast cancer classification research compiles breast cancer data and applies multiple machine learning approaches to learn predictive patterns from the features. The study evaluates supervised models such as Logistic Regression, Decision Tree, and Random Forest, using performance metrics that quantify classification quality and error types. Results are compared to identify which algorithm best distinguishes benign from malignant cases and can detect harmful cancer quickly. The methodology includes dataset preparation, preprocessing, and experimentation with model outputs.","CALIFORNIA STATE UNIVERSITY, NORTHRIDGE  \nBreast Cancer Classification using different Machine Learning Algorithms  \nA thesis submitted in partial fulfillment of the requirements for the degree of Master of Science in Computer Science  \nby  \nHarsh Prajapati  \nThe thesis of Harsh Prajapati is approved by:  \nDr. Mahdi Ebrahimi  \nDr. Robert McIlhenny  \nDr. Kyle Dewey, Chair  \nDate  \nDate  \nDate  \nCalifornia State University, Northridge  \nAcknowledgements  \nI would like to dedicate this thesis to my parents, family and friends for their continuous help and support throughout my education. I would like to express my heartfelt gratitude tomy advisor and committee chair Dr. Kyle Dewey for the continuous support of my master’s study and research, for his trust, patience, motivation, enthusiasm, and immense knowledge. I would also like to thank my thesis committee members Dr. Mahdi Ebrahimi and Dr. Robert McIlhenny for their support and guidance.  \nTable of Contents  \nSignature Page ii  \nAcknowledgement iii  \nList of Figures vi  \nAbstract vii  \nChapter 1: Introduction……………………………………………………………………1  \n1.1 Background………………………………………………………………………...1  \n1.2 Problem Statement…………………………………………………………………2  \nChapter 2: Dataset…………………………………………………………………………3  \n2.1 Features…………………………………………………………………………….6  \nChapter 3: Performance Metrics…………………………………………………………..8  \n3.1 Confusion Matrix…………………………………………………………………..8  \n3.2 False Positive Rate………………………………………………………………..10  \n3.3 False Negative Rate……………………………………………………………….11  \n3.4 Accuracy…………………………………………………………………………..12  \n3.5 Precision…………………………………………………………………………..12  \n3.6 Recall……………………………………………………………………………...13  \n3.7 Kohen Kappa……………………………………………………………………...13  \n3.8 ROC and AUC Curve……………………………………………………………..14  \n3.9 PR Curve………………………………………………………………………….16  \n3.10 MCC……………………………………………………………………………..18  \n3.11 F-1 Score………………………………………………………………………...18  \n3.12 F-Beta Score……………………………………………………………………..19  \nChapter 4: Models and Algorithms………………………………………………………22  \n4.1 Algorithms………………………………………………………………………...22  \n4.2 Machine Learning Algorithms……………………………………………………22  \n4.2.1 Supervised Algorithms………………………………………………….22  \n4.2.2Unsupervised Algorithms……………………………………………….23  \n4.2.3 Reinforcement Algorithms……………………………………………...24  \n4.3 Logistic Regression Algorithm……………………………………………………25  \n4.4 Decision Tree Algorithm………………………………………………………….29  \n4.5 Random Forest Algorithm………………………………………………………...32  \nChapter 5: Methods and Experiment Methodology……………………………………...35  \n5.1 Creating dataset…………………………………………………………………...36  \n5.2 Models and methodology…………………………………………………………41  \nA Working of Logistic Regression…………………………………………....41  \nB Working of Decision Tree………………………………………………….42  \nC Working of Random Forest………………………………………………...42  \nChapter-6: Result….….…………………………………………………………………..43  \nChapter-7: Conclusion……………………………………………………………………46  \nReferences………………………………………………………………………………..47  \nList of Figures  \nFigure 1: Original dataset snapshot from Kaggle (Excel)………………………………...4  \nFigure 2: Total size and first 5 entries in the dataset……………………………………....5  \nFigure 3: Confusion matrix………………………………………………………………..9  \nFigure 4: ROC curve......................................................................................................... 15  \nFigure 5: ROC and AUC curve combined........................................................................ 16  \nFigure 6: PR curve ............................................................................................................ 17  \nFigure 7: Example of confusion matrix ............................................................................ 20  \nFigure 8: Logistic Regression overview ........................................................................... 28  \nFigure 9: Decision Tree overview .................................................................................... 30  \nFigure 10: Random Forest overview ...............................................................................","cbCaiePhNFnBjjZX","https://ap.wps.com/l/cbCaiePhNFnBjjZX","pdf",1331970,1,55,"English","en",105,"# Chapter 1: Introduction\n## Background\n## Problem Statement\n# Chapter 2: Dataset\n## Features\n# Chapter 3: Performance Metrics\n## Confusion Matrix\n## False Positive Rate\n## False Negative Rate\n## Accuracy\n## Precision\n## Recall\n## Kohen Kappa\n## ROC and AUC Curve\n## PR Curve\n## MCC\n## F-1 Score\n## F-Beta Score\n# Chapter 4: Models and Algorithms\n## Algorithms\n## Machine Learning Algorithms\n## Supervised Algorithms\n## Unsupervised Algorithms\n## Reinforcement Algorithms\n## Logistic Regression Algorithm\n## Decision Tree Algorithm\n## Random Forest Algorithm\n# Chapter 5: Methods and Experiment Methodology\n## Creating dataset\n## Models and methodology\n## A Working of Logistic Regression\n## B Working of Decision Tree\n## C Working of Random Forest\n# Chapter 6: Result\n# Chapter 7: Conclusion\n# References","[{\"question\":\"Which machine learning algorithms are analyzed in the experiments?\",\"answer\":\"The experiments focus on Logistic Regression, Decision Tree, and Random Forest, comparing their classification results on the prepared dataset.\"}]","Breast Cancer Classification using different Machine Learning Algorithms - 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