[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-127481-en":3,"doc-seo-127481-105":30,"detail-sidebar-cat-0-en-105":92},{"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},127481,962085662650,"Jiven","https://ap-avatar.wpscdn.com/davatar_29158cc5080c5b710cf443261637dec0",8,"Research & Report","From Big Data to Machine Learning - An Empirical Application for Social Sciences","Machine learning, especially artificial neural network (ANN) based algorithms, is used to derive knowledge from large and heterogeneous data sets. The work examines how these methods can support social-science data analysis by presenting a sociological application to study relationships between variables. It also contrasts machine learning with traditional data analysis models, clarifying strengths and limitations while situating the discussion within supervised learning and the broader debate on progress in sociology.","From Big Data to Machine Learning: An Empirical Application for Social Sciences  \nBy Giovanni DiFranco * & Michele Santurro±  \nMachine learning (ML), and particularly algorithms based on artificial neural networks (ANNs), constitute afield of research lying at the intersection of different disciplines such as mathematics, statistics, computer science and neuroscience.  \nThis approach is characterized by the use of algorithms to extract knowledge from large and heterogeneous data sets. In this paper we will focus our attention on its possible applications in the social sciences and, in particular, on its potential in the data analysis procedures. In this regard, we will provide an example of application on sociological data to assess the impact of ML in the study of relationships between variables. Finally, we will compare the potential of ML with traditional data analysis models.  \nKeywords: machine learning, artificial neural networks, supervised learning, linear models, nonlinear models  \nIntroduction  \nML is an automatic learning process that takes place through the processing of usually very large data sets. The procedures ofthe past, defined with the “symbolic artificial intelligence” label, operated on algorithms constituted by a logical set of instructions by which a given output (usually called target) was encoded for all possible inputs. Contrarily, the new ML systems “learn” directly from data and estimate mathematical functions that discover representations of some input, or learn to link one or more inputs to one or more outputs in order to make predictionson new data (Jordan and Mitchell 2015).  \nIn recent years in various human sciences: economics (Varian 2014, Blumenstock et al. 2015, Athey and Imbens 2017, Mullainathan and Spiess 2017), political science (Baldassarri and Goldberg 2014, Bonikowski and DiMaggio 2016), sociology (Barocas and Selbst 2016, Evans and Aceves 2016, Baldassarri and Abascal 2017), communication science (Hopkins and King 2010, Grimmer and Stewart 2013, Bail 2014), etc., ML has started to be applied both in academic research and in areas related to the management of services provided by the public administration (Athey 2017, Berk et al. 2021) or by private companies.  \nOverall, many different approaches and tools are included under the ML label (Kleinberg et al. 2015). There is no consensus about how much depth a model requires to qualify as deep. Discussions with deep learning (DL) experts have not yet yielded a conclusive response to this question. However, DL can be safely understood as the set of models that involve a greater amount of composition of  \n*  \nProfessor, Department of Social and Economic Sciences, Sapienza University of Rome, Italy.±PhD Student, Department of Social and Economic Sciences, Sapienza University of Rome, Italy.  \neither learned functions or learned concepts than traditional ML does (Schmidhuber 2015, Goodfellow et al. 2016).  \nDL is not a breakthrough in the scientific sense, rather it is a relevant breakthrough in efficient coding that makes a difference in several contexts. In practical applications, DL is able to achieve higher accuracy on more complex tasks as compared with traditional ANNs, although it requires more computational resources. Furthermore, DL needs less manual interference to craft the right features or the suitable transformations of data. It performs exceptionally precise operations on data that come from different modalities, such as images, texts and videos (Schmidhuber 2015, Alpaydin 2016, Goodfellow et al. 2016).  \nSo, the choice between ML or DL algorithms depends on the problem to be analysed. If the problem is relatively simple, it is preferable to use ML based on ANNs with few layers of hidden units; if the problem is complex or requires the achievement of very specific and rigorous objectives, it is considered more useful to resort to DL.  \nHere we will only consider ANNs that use supervised ML algorithms. In the supervised ML the","cbCaiiNevcOQIq60","https://ap.wps.com/l/cbCaiiNevcOQIq60","pdf",177477,1,22,"English","en",105,"# Introduction\n## Machine learning and neural networks\n## Deep learning vs. traditional ML\n## Supervised vs. unsupervised learning\n## Aim and approach of the study","[{\"question\":\"What is the main focus of this paper in social sciences?\",\"answer\":\"It investigates potential applications of machine learning in social-science data analysis, including an empirical example using sociological data to assess relationships between variables.\"},{\"question\":\"How does supervised machine learning work according to the text?\",\"answer\":\"In supervised learning, the algorithm observes inputs and corresponding outputs, using the output as a target to learn and predict on new data.\"},{\"question\":\"What distinguishes deep learning from traditional machine learning?\",\"answer\":\"Deep learning involves greater composition of learned functions or concepts and can deliver higher accuracy on complex tasks, though it needs more computational resources.\"}]","From Big Data to Machine Learning - 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