[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-120768-en":3,"doc-seo-120768-105":30,"detail-sidebar-cat-0-en-105":91},{"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":20,"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},120768,34359740700684,"Finn","https://ap-avatar.wpscdn.com/avatar/1f400023980c374ae676?_k=1777273430885731487",8,"Research & Report","Effect of Feature Selection on the Accuracy of Machine Learning Model","Real-world data science projects rarely have datasets where every available feature is useful for model building. Feature selection focuses on choosing a relevant subset of attributes and removing irrelevant or redundant variables to reduce complexity and prevent performance degradation. An experiment trains K-Nearest Neighbors, Decision Tree, and Multi-layer Perceptron using all features, then repeats training after applying feature selection. Accuracy improves markedly, demonstrating that feature selection strongly influences classification model performance.","INTERNATIONAL JOURNAL OF MULTIDISCIPLINARY RESEARCH AND ANALYSIS  \nISSN(print): 2643-9840, ISSN(online): 2643-9875 Volume 06 Issue 09 September 2023  \nDOI: 10.47191/ijmra/v6-i9-66, Impact Factor: 7.022 Page No. 4460-4466  \nEffect of Feature Selection on the Accuracy of Machine Learning Model  \nAsst. Professor Mohammad Salim Hamdard1, Asst. Professor Hedayatullah Lodin2  \n1,2 Faculty of Computer Science, Kabul University  \nABSTRACT: In real life data science problems, it’s almost rare that all the features in the dataset are useful for building a model. In machine learning, feature selection is the process of selecting a subset of relevant features or attributes for constructing a model. Removing irrelevant and redundant features and, selecting relevant features will improve the accuracy of a machine learning model. Furthermore, adding unnecessary variables to a model increases the overall complexity of the model. Our experiment indicates that the accuracy of a classification model is highly affected by the process of feature selection. We train three algorithms (K-Nearest Neighbors, Decision Tree, Multi-layer Perceptron) by selecting all the features and we got accuracies 49%, 84% and 71% accordingly. After doing some feature selection without any logical changes in models code the accuracy scores jumped to 82%, 86% and 78% accordingly which is quite impressive.  \nKEYWORDS: Machine Learning, Feature Selection, Accuracy, Dimensionality Reduction, Classification  \n1. INTRODUCTION  \nFeature selection is one of the core concept in machine learning which hugely impacts the performance of your model, especially in datasets with many input variables and a low variance [1] . The goal of feature selection process in machine learning is to find the best set of features that allows one to build optimized models that will have a great accuracy score [2] . The input data that we use to train our machine learning model have a huge influence on the model’s performance. The increase in dimensionality of data can lead to big challenges in both supervised and unsupervised learning process. Training your model with redundant features reduces the model’s overall capability and may also reduce model’s accuracy. Moreover, adding extra variables to a model increases the overall complexity of the model [3] . Performing feature selection offers several benefits, it reduces overfitting, improves accuracy, and reduces training time. This paper will provide a great analysis of the importance of featureselection in constructing an optimized machine learning model [4] .  \n1.1 RESEARCH QUESTION  \nIn this research paper we will study the impact of feature selection process on the accuracy of a machine learning model by using three different machine learning algorithms (KNN, Decision Tree, Multi-layer Perceptron) . We aim to answer the following research questions:  \n􀁸 Does the increase in input variables with a low variance decrease the overall capability of a machine learning model?  \n􀁸 Does the feature selection process improve the accuracy of machine learning model compared to using all features?  \n2. BACKGROUND  \nThe field of machine learning is concerned with automated discoveries of regularities in data with use of computer algorithms. These regularities can then be used to take actions, such as classifying data into different categories or making predictions. Asthe data may be of different kinds, the machine learning algorithms that learn from these data may differ too [5] . The machine learning algorithms used for conducting this research are discussed below.  \n2.1 K-Nearest Neighbors  \nK-Nearest Neighbors is one of the simplest machine learning algorithms based on supervised learning technique. It is effective for classification as well as regression. However, it is more widely used for classification problems. It is a lazy learner algorithm because it does not learn from the training set immediately [6] . In case of KNN algorithm, a particular valu","cbCaiiYmTchVtyeZ","https://ap.wps.com/l/cbCaiiYmTchVtyeZ","pdf",837281,1,7,"English","en",105,"# Introduction\n## Research Question\n# Background\n## K-Nearest Neighbors\n## Decision Tree\n## Artificial Neural Network","[{\"question\":\"What is feature selection in machine learning?\",\"answer\":\"Feature selection is the process of choosing a subset of relevant features or attributes for constructing a model. It removes irrelevant and redundant variables to improve performance and reduce complexity.\"},{\"question\":\"How does feature selection affect model accuracy in the paper?\",\"answer\":\"The study shows accuracy improves significantly after feature selection for three algorithms: K-Nearest Neighbors, Decision Tree, and Multi-layer Perceptron. The reported accuracy jumps from 49%/84%/71% to 82%/86%/78% respectively.\"},{\"question\":\"Why does adding unnecessary variables increase model complexity?\",\"answer\":\"Adding extra variables increases the overall complexity of the model, which can harm learning. The paper also notes that removing redundant features can reduce overfitting, improve accuracy, and lower training time.\"}]","Effect of Feature Selection on the Accuracy of Machine Learning Model | PDF",1785731938,18,{"code":4,"msg":31,"data":32},"ok",{"site_id":24,"language":23,"slug":33,"title":13,"keywords":34,"description":14,"schema_data":35,"social_meta":86,"head_meta":88,"extra_data":90,"updated_unix":28},"effect-of-feature-selection-on-the-accuracy-of-machine-learning-model","",{"@graph":36,"@context":85},[37,54,68],{"@type":38,"itemListElement":39},"BreadcrumbList",[40,44,48,51],{"item":41,"name":42,"@type":43,"position":20},"https://docshare.wps.com","Home","ListItem",{"item":45,"name":46,"@type":43,"position":47},"https://docshare.wps.com/document/","Document",2,{"item":49,"name":12,"@type":43,"position":50},"https://docshare.wps.com/document/research-report/",3,{"item":52,"name":13,"@type":43,"position":53},"https://docshare.wps.com/document/effect-of-feature-selection-on-the-accuracy-of-machine-learning-model/120768/",4,{"url":52,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":23,"description":14,"dateModified":62,"datePublished":62,"encodingFormat":61,"isAccessibleForFree":63,"interactionStatistic":64},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":41,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-08-03",true,{"@type":65,"interactionType":66,"userInteractionCount":20},"InteractionCounter",{"@type":67},"ViewAction",{"@type":69,"mainEntity":70},"FAQPage",[71,77,81],{"name":72,"@type":73,"acceptedAnswer":74},"What is feature selection in machine learning?","Question",{"text":75,"@type":76},"Feature selection is the process of choosing a subset of relevant features or attributes for constructing a model. It removes irrelevant and redundant variables to improve performance and reduce complexity.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"How does feature selection affect model accuracy in the paper?",{"text":80,"@type":76},"The study shows accuracy improves significantly after feature selection for three algorithms: K-Nearest Neighbors, Decision Tree, and Multi-layer Perceptron. The reported accuracy jumps from 49%/84%/71% to 82%/86%/78% respectively.",{"name":82,"@type":73,"acceptedAnswer":83},"Why does adding unnecessary variables increase model complexity?",{"text":84,"@type":76},"Adding extra variables increases the overall complexity of the model, which can harm learning. 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