Showing items with similar title or keywords.

• Eight Simple Guidelines for Improved Understanding of Transformations and Nonlinear Effects 

  Rönkkö, Mikko; Aalto, Eero; Tenhunen, Henni; Aguirre-Urreta, Miguel I. (SAGE Publications, 2022)

  Transforming variables before analysis or applying a transformation as a part of a generalized linear model are common practices in organizational research. Several methodological articles addressing the topic, either ...

• Intelligent solutions for real-life data-driven applications 

  Ivannikova, Elena (University of Jyväskylä, 2017)

  The subject of this thesis belongs to the topic of machine learning or, specifically, to the development of advanced methods for regression analysis, clustering, and anomaly detection. Industry is constantly seeking ...

• Comparing the forecasting performance of logistic regression and random forest models in criminal recidivism 

  Aaltonen, Olli-Pekka (2016)

  Rikosseuraamusalalla on viime vuosina kehitetty uusintarikollisuutta ennustavia malleja (Tyni, 2015), jotka perustuvat tyypillisesti rekisteripohjaisiin mittareihin, jotka mittaavat mm. tuomitun sukupuolta, ikää, rikostaustaa ...

• Do Randomized Algorithms Improve the Efficiency of Minimal Learning Machine? 

  Linja, Joakim; Hämäläinen, Joonas; Nieminen, Paavo; Kärkkäinen, Tommi (MDPI AG, 2020)

  Minimal Learning Machine (MLM) is a recently popularized supervised learning method, which is composed of distance-regression and multilateration steps. The computational complexity of MLM is dominated by the solution of ...

• Comparison of machine learning and logistic regression as predictive models for adverse maternal and neonatal outcomes of preeclampsia : A retrospective study 

  Zheng, Dongying; Hao, Xinyu; Khan, Muhanmmad; Wang, Lixia; Li, Fan; Xiang, Ning; Kang, Fuli; Hamalainen, Timo; Cong, Fengyu; Song, Kedong; Qiao, Chong (Frontiers Media SA, 2022)

  Introduction: Preeclampsia, one of the leading causes of maternal and fetal morbidity and mortality, demands accurate predictive models for the lack of effective treatment. Predictive models based on machine learning ...