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Improvements span data preprocessing, feature engineering, model selection, and training methods. The accuracy of the original K-Nearest Neighbors (KNN) model increases from 30% to 45%. Key steps include using a TF-IDF vectorizer with a customized stop word list and pattern-based tokenization, plus comparative evaluation against models such as SVM and Random 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Peter","Person",{"url":74,"name":95,"@type":96},"DocShare","Organization","application/pdf","2026-11-01","2026-09-17",true,{"@type":102,"interactionType":103,"userInteractionCount":56},"InteractionCounter",{"@type":104},"ViewAction",{"@type":106,"mainEntity":107},"FAQPage",[108,114,118],{"name":109,"@type":110,"acceptedAnswer":111},"What is the main goal of this research on the MBTI dataset?","Question",{"text":112,"@type":113},"It aims to improve prediction accuracy and generalization for MBTI personality types using an optimized machine learning pipeline on the Kaggle dataset.","Answer",{"name":115,"@type":110,"acceptedAnswer":116},"Which key changes improve the original KNN model’s performance?",{"text":117,"@type":113},"The study refines data preprocessing and feature engineering, including replacing Count Vectorizer with TF-IDF (TfidfVectorizer), using a customized stop word list, and applying pattern-based tokenization and processing optimization.",{"name":119,"@type":110,"acceptedAnswer":120},"How is the improved KNN model validated against other methods?",{"text":121,"@type":113},"It includes a comparative analysis with other classification models such as Support Vector Machines (SVM) and Random Forest, using multiple metrics to assess performance gains.","https://schema.org",{"og:url":83,"og:type":124,"og:title":65,"og:site_name":95,"og:description":67},"article",{"robots":126,"canonical":83},"index,follow",{"doc_id":128,"site_id":62},293382,1789663376,{"code":4,"msg":5,"data":131},{"doc_id":128,"user_id":132,"nickname":92,"user_avatar":133,"doc_module":4,"category_id":39,"category_name":40,"doc_title":65,"doc_description":67,"doc_content":134,"file_id":135,"file_url":136,"file_type":137,"file_size":138,"view_count":56,"is_deleted":4,"is_public":8,"is_downloadable":8,"audit_status":8,"page_count":34,"language":139,"language_code":63,"site_id":62,"html_lang":63,"table_of_contents":140,"faqs":141,"seo_title":142,"seo_description":67,"update_tm":129,"read_time":143,"preview_page_count":4},1374402524268,"https://ap-avatar.wpscdn.com/davatar_3d24733baf745e90a7e4bdd5f77d97b2","# Improving Machine Learning Methods to Enhance PredictionAccuracy of MBTI Dataset\n\nKaitao Yan  \nHainan International College,Minzu University of China,27 Zhongguancun South Street,Haidian District,Beijing,China  \nKeywords:Myers-Briggs Type Indicator(MBTI),Model Development,Hyperparameter Optimization,NaturalLanguage Processing(NLP)  \nAbstract:This study presents an optimized machine learning approach to enhance the accuracy and generalization ofpredicting the Myers-Briggs Type Indicator(MBTI)dataset from Kaggle.Improvements across severalmodules—namely data preprocessing,feature engineering,model selection,and training methods—resultedin an increase in the accuracy of the original K-Nearest Neighbors(KNN)model from 30%to 45%.Keyenhancements in this study include the use of a Term Frequency-Inverse Document Frequency Vectorizer(TfidfVectorizer)instead of a Count Vectorizer for more precise feature extraction,the refinement of textprocessing through a customized stop word list and a pattern-based token signifier,and the optimization ofdata processing.Additionally,a comparative analysis of various classification models,such as SupportVector Machines(SVMs)and Random Forest models,is conducted to validate the performance of theimproved KNN model across several metrics.The advancements of the enhanced KNN model underscorethe effectiveness of the optimization strategy in improving the original KNN model's ability to accuratelypredict MBTI personality types.  \nOver time,the interest of researchers in the fieldsof natural language processing and social scienceshas also come to the aspect of automatic personalityprediction on the use of social media(Nguyen,Doogruöz,Rosé,&De Jong,2016).Using machinelearning methods,researchers have successfullyanalyzed and forecasted MBTI personality types byprocessing text data.Machine learning techniqueshave played a crucial role in MBTI prediction,starting with the early work by Golbeck et al.,wholeveraged user-displayed information on Twitter toclassify MBTI types(Golbeck,Robles,Edmondson,&Turner,2011).Later,Hernandez and Knightexplored advanced neural network models,includingRecurrent Neural Network(RNN)and LongShort-Term Memory(LSTM),to build a moresophisticated MBTI predictor using data from socialplatforms (Hernandez &Knight,2017).Thisprogression highlights the growing influence ofmachine learning in MBTI-related research.  \n## 1 INTRODUCTION\n\nAs most of you know,the Myers-Briggs TypeIndicator(MBTI)is heavily used as a personalityassessment tool in the field of personal growth,industry inquiries or team development(Tareaf,2023).It categorizes people into differentpersonality types,16 in total.That is,how peopleexpress certain traits of themselves such asintroversion and extroversion,feeling and intuition,thinking and feeling,judging and perceiving,judging and perceiving(Myers &McCaulley,1989).From the Social Media Trends Report published byCommunications of the Association for ComputingMachinery,there are 3.8 billion active social mediausers globally until January 2020,which is expectedto grow by 9.2%per year(Violino,2020).Thismeans that as social media becomes more popular,the rate of information growth will increase.Peopleuse social media to share a wide variety of aspects oftheir daily lives,work experiences,and studyprogress,and in some scenarios,this information canbe used to describe an individual's behavior and theirpersonality(Christian,Suhartono,Chowanda,&Zamli,2021).  \nIn fact,the KNN model is widely used as a basicmodel for MBTI prediction,but there is a certaingap in accuracy compared to the BidirectionalEncoder Representations from Transformers(BERT)model.This research aims to enhance the model's  \n466  \nYan and K.  \nprediction accuracy on the widely-used KaggleMBTI dataset by refining crucial stages,includingdata preprocessing,feature extraction,modelselection,and hyperparameter optimization,bringingits performance closer to that of fine-tuned KNN andweighted KNN algorithms(Shafi,2","cbCaibcr8BC1fZsQ","https://ap.wps.com/l/cbCaibcr8BC1fZsQ","pdf",883000,"English","# Improving Machine Learning Methods to Enhance PredictionAccuracy of MBTI Dataset\n# 1 INTRODUCTION\n# 2 LITERATURE REVIEW","[{\"question\":\"What is the main goal of this research on the MBTI dataset?\",\"answer\":\"It aims to improve prediction accuracy and generalization for MBTI personality types using an optimized machine learning pipeline on the Kaggle dataset.\"},{\"question\":\"Which key changes improve the original KNN model’s performance?\",\"answer\":\"The study refines data preprocessing and feature engineering, including replacing Count Vectorizer with TF-IDF (TfidfVectorizer), using a customized stop word list, and applying pattern-based tokenization and processing optimization.\"},{\"question\":\"How is the improved KNN model validated against other methods?\",\"answer\":\"It includes a comparative analysis with other classification models such as Support Vector Machines (SVM) and Random Forest, using multiple metrics to assess performance gains.\"}]","Improving Machine Learning Methods to Enhance PredictionAccuracy of MBTI Dataset | PDF",18]