{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,24]],"date-time":"2026-03-24T03:15:45Z","timestamp":1774322145174,"version":"3.50.1"},"publisher-location":"Berlin, Heidelberg","reference-count":23,"publisher":"Springer Berlin Heidelberg","isbn-type":[{"value":"9783662584842","type":"print"},{"value":"9783662584859","type":"electronic"}],"license":[{"start":{"date-parts":[[2018,12,18]],"date-time":"2018-12-18T00:00:00Z","timestamp":1545091200000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2019]]},"DOI":"10.1007\/978-3-662-58485-9_6","type":"book-chapter","created":{"date-parts":[[2018,12,17]],"date-time":"2018-12-17T03:18:15Z","timestamp":1545016695000},"page":"46-57","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":15,"title":["Selection and Application of Machine Learning- Algorithms in Production Quality"],"prefix":"10.1007","author":[{"given":"Jonathan","family":"Krau\u00df","sequence":"first","affiliation":[]},{"given":"Maik","family":"Frye","sequence":"additional","affiliation":[]},{"given":"Gustavo Teodoro D\u00f6hler","family":"Beck","sequence":"additional","affiliation":[]},{"given":"Robert H.","family":"Schmitt","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2018,12,18]]},"reference":[{"key":"6_CR1","unstructured":"Michael Driscoll (2011) Building data startups: Fast, big, and focused: Low costs and cloud tools are empowering new data startups. http:\/\/radar.oreilly.com\/2011\/08\/building-data-startups.html . Accessed 14 May 2018"},{"key":"6_CR2","unstructured":"Peter Sondergaard (2011) Gartner Says Worldwide Enterprise IT Spending to Reach $2.7 Trillion in 2012. https:\/\/www.gartner.com\/newsroom\/id\/1824919 . Accessed 14 May 2018"},{"key":"6_CR3","unstructured":"Piatetsky-Shapiro G (2014) What main methodology are you using for your analytics, data mining, or data science projects? https:\/\/www.kdnuggets.com\/polls\/2014\/analytics-data-mining-data-science-methodology.html . Accessed14 May 2018"},{"key":"6_CR4","unstructured":"Datong P. Zhou, Qie Hu, Claire J. Tomlin (2017) Quantitative comparison of data-driven and physics-based models for commercial building HVAC systems"},{"key":"6_CR5","unstructured":"Pete Chapman, Julian Clinton, Randy Kerber, Thomas Khabaza, Thomas Reinartz, Colin Shearer and R\u00fcdiger Wirth CRISP-DM: Step-by-step data mining guide"},{"key":"6_CR6","unstructured":"Scikit-learn Developers (2018) Decision Tree Classifier. http:\/\/scikitlearn.org\/stable\/modules\/generated\/sklearn.tree.DecisionTreeClassifier.html . Accessed 14 May 2018"},{"key":"6_CR7","unstructured":"Gregory Piatetsky (2018) Survey regarding data mining platforms: What software you used for Analytics, Data Mining, Data Science, Machine Learning projects in the past 12 months? http:\/\/vote.sparklit.com\/poll.spark\/203792 . Accessed 14 May 2018"},{"key":"6_CR8","unstructured":"Gregory Piatetsky (2018) Gainers and Losers in Gartner 2018 Magic Quadrant for Data Science and Machine Learning Platforms. https:\/\/www.kdnuggets.com\/2018\/02\/gartner-2018-mq-data-science-machine-learningchanges.html . Accessed 14 May 2018"},{"key":"6_CR9","unstructured":"Gregory Piatetsky (2017) Forrester vs Gartner on Data Science Platforms and Machine Learning Solutions. https:\/\/www.kdnuggets.com\/2017\/04\/forrestergartner-data-science-platforms-machine-learning.html . Accessed 14 May 2018"},{"key":"6_CR10","unstructured":"Scikit-learn Developers (2018) Decision Trees. http:\/\/scikit-learn.org\/stable\/modules\/tree.html . Accessed 14 May 2018"},{"key":"6_CR11","unstructured":"Dr. Jason Brownlee (2017) What is the Difference Between a Parameter and a Hyperparameter? https:\/\/machinelearningmastery.com\/difference-between-a-parameter-and-a-hyperparameter\/ . Accessed 14 May 2018"},{"key":"6_CR12","doi-asserted-by":"crossref","unstructured":"Rafael G. Mantovani, Tom\u00e1\u0161 Horv\u00e1th, Ricardo Cerri, Joaquin Vanschoren, Andr\u00e9 C.P.L.F. de_Carvalho (2016) Hyperparameter Tuning of a Decision Tree Induction Algorithm. IEEE, Piscataway, NJ","DOI":"10.1109\/BRACIS.2016.018"},{"key":"6_CR13","unstructured":"Pawel Matuszyk, Rene Tatua Castillo, Daniel Kottke A Comparative Study on Hyperparameter Optimization for Recommender Systems"},{"key":"6_CR14","unstructured":"Mohamed Bekkar, Dr.Hassiba Kheliouane Djemaa, Dr.Taklit Akrouf Alitouche Evaluation Measures for Models Assessment over Imbalanced Data Sets. 2013"},{"key":"6_CR15","doi-asserted-by":"crossref","unstructured":"Patrick Koch, Brett Wujek, Oleg Golovidov et al. (2017) Automated Hyperparameter Tuning for Effective Machine Learning","DOI":"10.1145\/3219819.3219837"},{"issue":"4","key":"6_CR16","doi-asserted-by":"publisher","first-page":"358","DOI":"10.1002\/humu.21445","volume":"32","author":"Janita Thusberg","year":"2011","unstructured":"Thusberg J, Olatubosun A, Vihinen M (2011) Performance of mutation pathogenicity prediction methods on missense variants. Hum Mutat 32(4): 358\u2013368.doi: 10.1002\/humu.21445","journal-title":"Human Mutation"},{"key":"6_CR17","unstructured":"(2018) Jupyter Notebook. https:\/\/jupyter.readthedocs.io\/en\/latest\/architecture\/how_jupyter_ipython_work.html . Accessed 14 May 2018"},{"key":"6_CR18","doi-asserted-by":"crossref","unstructured":"Mariscal G, Marb\u00e1n \u00d3, Fern\u00e1ndez C (2010) A survey of data mining and knowledge discovery process models and methodologies. The Knowledge Engineering Review 25(02): 137\u2013166. doi: 10.1017\/S0269888910000032","DOI":"10.1017\/S0269888910000032"},{"key":"6_CR19","doi-asserted-by":"crossref","unstructured":"Azevedo A, Santos MF (2008) KDD, SEMMA and CRISP-DM: a parallel overview July 24-26, 2008. Proceedings. In: Abraham A (ed) IADIS European Conference on Data Mining 2008, Amsterdam, The Netherlands, July 24-26, 2008. Proceedings. IADIS, pp 182\u2013185","DOI":"10.22233\/20412495.090108.26"},{"key":"6_CR20","unstructured":"Piatetsky-Shapiro G (2017) Top Data Science and Machine Learning Methods Used in 2017. https:\/\/www.kdnuggets.com\/2017\/12\/top-data-science-machinelearning-methods.html . Accessed 14 May 2018"},{"key":"6_CR21","unstructured":"pakalra, olprod, OpenLocalizationService (2017) Machine Learning \u2013 Cheat Sheet f\u00fcr Algorithmen f\u00fcr Microsoft Azure Machine Learning Studio. https:\/\/docs.microsoft.com\/de-de\/azure\/machine-learning\/studio\/algorithmcheat-sheet . Accessed 14 May 2018"},{"key":"6_CR22","unstructured":"Basili VR, Caldiera G, Rombach HD (1994) The Goal Question Metric Approach. In: Encyclopedia of Software Engineering. Wiley"},{"key":"6_CR23","doi-asserted-by":"publisher","first-page":"161","DOI":"10.1007\/978-3-642-23229-9_8","volume-title":"Recent Advances in Intelligent Engineering Systems","author":"Erik Pitzer","year":"2012","unstructured":"Pitzer E, Affenzeller M (2012) A Comprehensive Survey on Fitness Landscape Analysis. In: Fodor J, Klempous R, Su\u00e1rez Araujo CP (eds) Recent Advances in Intelligent Engineering Systems, vol 378. Springer Berlin Heidelberg, Berlin, Heidelberg, pp 161\u2013191"}],"container-title":["Technologien f\u00fcr die intelligente Automation","Machine Learning for Cyber Physical Systems"],"original-title":[],"link":[{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-662-58485-9_6","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2019,11,7]],"date-time":"2019-11-07T22:45:59Z","timestamp":1573166759000},"score":1,"resource":{"primary":{"URL":"http:\/\/link.springer.com\/10.1007\/978-3-662-58485-9_6"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2018,12,18]]},"ISBN":["9783662584842","9783662584859"],"references-count":23,"URL":"https:\/\/doi.org\/10.1007\/978-3-662-58485-9_6","relation":{},"ISSN":["2522-8579","2522-8587"],"issn-type":[{"value":"2522-8579","type":"print"},{"value":"2522-8587","type":"electronic"}],"subject":[],"published":{"date-parts":[[2018,12,18]]}}}