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Accuracy, training time, and testing time were evaluated to measure model effectiveness and efficiency. The dataset was reduced to remove redundant, strongly correlated features, then visualized and tested with shallow techniques and a simple neural network. Cross-validation ensured repeatability of results. Random Forest achieved the best average accuracy (92.61%) with reasonable train/test times.",{"@graph":69,"@context":122},[70,84,105],{"@type":71,"itemListElement":72},"BreadcrumbList",[73,77,79,82],{"item":74,"name":75,"@type":76,"position":8},"https://docshare.wps.com","Home","ListItem",{"item":78,"name":9,"@type":76,"position":14},"https://docshare.wps.com/document/",{"item":80,"name":30,"@type":76,"position":81},"https://docshare.wps.com/document/technology/",3,{"item":83,"name":65,"@type":76,"position":19},"https://docshare.wps.com/document/python-machine-learning-dry-beans-classification-case/290774/",{"url":83,"name":65,"@type":85,"image":86,"author":91,"headline":65,"publisher":94,"fileFormat":97,"inLanguage":63,"description":67,"dateModified":98,"datePublished":99,"encodingFormat":97,"isAccessibleForFree":100,"interactionStatistic":101},"DigitalDocument",{"url":87,"@type":88,"width":89,"height":90},"https://docshare.wps.com/thumbnails/python-machine-learning-dry-beans-classification-case/290774.png","ImageObject",300,407,{"name":92,"@type":93},"River Wang","Person",{"url":74,"name":95,"@type":96},"DocShare","Organization","application/pdf","2026-10-17","2026-09-17",true,{"@type":102,"interactionType":103,"userInteractionCount":34},"InteractionCounter",{"@type":104},"ViewAction",{"@type":106,"mainEntity":107},"FAQPage",[108,114,118],{"name":109,"@type":110,"acceptedAnswer":111},"What problem does the document address?","Question",{"text":112,"@type":113},"It addresses automatic classification of dry bean species using geometric feature datasets through machine learning and deep learning.","Answer",{"name":115,"@type":110,"acceptedAnswer":116},"How was the dataset prepared before modeling?",{"text":117,"@type":113},"The original dataset was reduced to eliminate redundant features that were strongly correlated or duplicated, then the processed data was visualized for further analysis.",{"name":119,"@type":110,"acceptedAnswer":120},"Which model performed best for dry beans classification?",{"text":121,"@type":113},"Random Forest produced the best results, reaching an average accuracy of 92.61% with reasonable training and testing times.","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},290774,1790994098,{"code":4,"msg":5,"data":131},{"doc_id":128,"user_id":132,"nickname":92,"user_avatar":133,"doc_module":4,"category_id":29,"category_name":30,"doc_title":65,"doc_description":67,"doc_content":134,"file_id":135,"file_url":136,"file_type":137,"file_size":138,"view_count":34,"is_deleted":4,"is_public":8,"is_downloadable":8,"audit_status":8,"page_count":46,"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":143,"read_time":31},1099514067438,"https://ap-avatar.wpscdn.com/avatar/100002539ee87300030?x-image-process=image/resize,m_fixed,w_180,h_180&k=1780474512215547542","Python Machine Learning.  \nDry Beans Classification Case  \nGrzegorz Słowiński*  \nWarsaw School of Computer Science, Warsaw, Poland  \nAbstract  \nA dataset containing over 13k samples of dry beans geometric features was analyzed using machine learning (ML) and deep learning (DL) techniques with the goal to automatically classify the bean species. Performance in terms of accuracy, train and test time was analyzed. First the original dataset was reduced to eliminate redundant features (too strongly correlated and echoing others) . Then the dataset was visualized and analyzed with a few shallow learning techniques and simple artificial neural network. Cross validation was used to check the learning process repeatability. Influence of data preparation (dimension reduction) on shallow learning techniques were observed. In case of Multilayer Perceptron 3 activation functions were tried: ReLu, ELU and sigmoid. Random Forest appeared to be the best model for dry beans classification task reaching average accuracy reaching 92.61% with reasonable train and test times.  \nKeywords – machine learning, deep learning, data dimension reduction, activation function  \n* E-mail: [gslowinski@ms.wwsi.edu.pl](gslowinski@ms.wwsi.edu.pl)  \nManuscript received December 14, 2023.  \n1. Introduction  \nSince 2010, there has been an intensive development of artificial intelligence, including machine learning and deep learning (artificial neural networks) . There is a shortage of specialists in a broad field of computer science and engineering. A similar deficit of specialists exists in the area of the artificial intelligence. The prevalence of demand over supply causes that the salaries of data scientists and deep learning engineers are significantly higher than the average in the economy [1] .  \nTwo programming languages have gained particular popularity in this area: R and Python [2] . This work tries to provide a (necessarily limited) introduction to the subject of the artificial intelligence and machine learning, using the case of dry beans classification.  \n1.1. Terms: Artificial Intelligence, Machine Learning and Deep Machine Learning  \nThe term Artificial Intelligence was probably created and used for the first time in 1955 by John McCarthy [3] in submission of a summer research project. The purpose of the project was to check the hypotheses as to whether human learning or any other manifestation of intelligence can be described by the rules so precisely that it will be possible to create a machine simulating this process. Artificial intelligence (AI) is abroad and blurred concept. There is no precise definition of this term. On the one hand, the term artificial intelligence is sometimes used to describe relatively simple algorithms that are assumed to imitate human thinking and acting, on the other hand, it addresses not yet existing a self-aware IT solution [4] .  \nThe Oxford Reference defines artificial intelligence as follows: “The theory and development of computer systems able to perform tasks normally requiring human intelligence, such as visual perception, speech recognition, decision-making, and translation between languages” [5] . It seems that in practice the term artificial intelligence also refers to an IT solution that simulates (imitates) human thinking. The literature distinguishes certain degrees of AI development: narrow AI, general AI, and artificial superintelligence [6] . It should not be forgotten that the term AI orthe adjective intelligent itself is sometimes used in exaggeration, for the purpose of marketing, to use positive connotation of the word “intelligent” to raise the  \nimportance of the offered product or service in the eyes of a potential customer. Hence, one can meet intelligent (by name): houses, cars, household appliances and other solutions in which the element of “intelligence” may be debatable.  \n1.2. Machine Learning  \nMachine Learning is part ofAI. Dependence between AI, machine learning (ML) and deep learning","cbCairPih4Om3bju","https://ap.wps.com/l/cbCairPih4Om3bju","pdf",721893,"English","# Introduction\n## Terms: Artificial Intelligence, Machine Learning and Deep Machine Learning\n## Machine Learning\n## Shallow learning versus deep learning","[{\"question\":\"What problem does the document address?\",\"answer\":\"It addresses automatic classification of dry bean species using geometric feature datasets through machine learning and deep learning.\"},{\"question\":\"How was the dataset prepared before modeling?\",\"answer\":\"The original dataset was reduced to eliminate redundant features that were strongly correlated or duplicated, then the processed data was visualized for further analysis.\"},{\"question\":\"Which model performed best for dry beans classification?\",\"answer\":\"Random Forest produced the best results, reaching an average accuracy of 92.61% with reasonable training and testing times.\"}]","Python Machine Learning - Dry Beans Classification Case | PDF",1789642545]