Visual techniques to compare predictive models

Paolo Buono; Enrico Bertini; Alessandra Legretto; Maria F. Costabile

doi:10.1145/3351995.3352035

Visual techniques to compare predictive models

Paolo Buono, Enrico Bertini, Alessandra Legretto, Maria F. Costabile

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Abstract

Predictive analysis is an important part of data analysis. Predictive models, based on Statistics or Machine Learning, are increasingly used to estimate, with a certain probability, future values of the variables that describe a phenomenon. Different models produce different results on a same dataset; thus, several models should be compared in order to identify the most suitable one. The paper is part of a larger research that aims at providing interactive visualizations that help the analysts to compare predictive models and to select the model that best fits the data. Specifically, two visualizations are presented, which support the analysts in performing some tasks of the Keim's Visual Analytics Mantra.

Original language	English (US)
Title of host publication	CHItaly 2019 - Proceedings of the 13th Biannual Conference of the Italian SIGCHI Chapter Designing the Next Interaction
Publisher	Association for Computing Machinery
ISBN (Electronic)	9781450371902
DOIs	https://doi.org/10.1145/3351995.3352035
State	Published - Sep 23 2019
Event	13th Biannual Conference of the Italian SIGCHI Chapter Designing the Next Interaction, CHItaly 2019 - Padua, Italy Duration: Sep 23 2019 → Sep 25 2019

Publication series

Name	ACM International Conference Proceeding Series

Conference

Conference	13th Biannual Conference of the Italian SIGCHI Chapter Designing the Next Interaction, CHItaly 2019
Country/Territory	Italy
City	Padua
Period	9/23/19 → 9/25/19

Keywords

Comparison Matrix
Pie-chart Matrix
Visual Analytics

ASJC Scopus subject areas

Software
Human-Computer Interaction
Computer Vision and Pattern Recognition
Computer Networks and Communications

Access to Document

10.1145/3351995.3352035

Cite this

Buono, P., Bertini, E., Legretto, A., & Costabile, M. F. (2019). Visual techniques to compare predictive models. In CHItaly 2019 - Proceedings of the 13th Biannual Conference of the Italian SIGCHI Chapter Designing the Next Interaction Article a2 (ACM International Conference Proceeding Series). Association for Computing Machinery. https://doi.org/10.1145/3351995.3352035

Visual techniques to compare predictive models. / Buono, Paolo; Bertini, Enrico; Legretto, Alessandra et al.
CHItaly 2019 - Proceedings of the 13th Biannual Conference of the Italian SIGCHI Chapter Designing the Next Interaction. Association for Computing Machinery, 2019. a2 (ACM International Conference Proceeding Series).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Buono, P, Bertini, E, Legretto, A & Costabile, MF 2019, Visual techniques to compare predictive models. in CHItaly 2019 - Proceedings of the 13th Biannual Conference of the Italian SIGCHI Chapter Designing the Next Interaction., a2, ACM International Conference Proceeding Series, Association for Computing Machinery, 13th Biannual Conference of the Italian SIGCHI Chapter Designing the Next Interaction, CHItaly 2019, Padua, Italy, 9/23/19. https://doi.org/10.1145/3351995.3352035

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abstract = "Predictive analysis is an important part of data analysis. Predictive models, based on Statistics or Machine Learning, are increasingly used to estimate, with a certain probability, future values of the variables that describe a phenomenon. Different models produce different results on a same dataset; thus, several models should be compared in order to identify the most suitable one. The paper is part of a larger research that aims at providing interactive visualizations that help the analysts to compare predictive models and to select the model that best fits the data. Specifically, two visualizations are presented, which support the analysts in performing some tasks of the Keim's Visual Analytics Mantra.",

keywords = "Comparison Matrix, Pie-chart Matrix, Visual Analytics",

author = "Paolo Buono and Enrico Bertini and Alessandra Legretto and Costabile, {Maria F.}",

note = "Funding Information: This work is funded by Italian MIUR through PON Ricerca e In-novazione 2014-2020 - Asse I {"}Investimenti in capitale umano{"} - Azione I.1 {"}Dottorati Innovativi con caratterizzazione industriale{"} (CUP: H96D17000040006) and Electronic Shopping & Home delivery of Edible goods with Low environmental Footprint (E-SHELF), POR Puglia FESR-FSE 2014-2020 (ID: OSW3NO1). Publisher Copyright: {\textcopyright} 2019 Association for Computing Machinery.; 13th Biannual Conference of the Italian SIGCHI Chapter Designing the Next Interaction, CHItaly 2019 ; Conference date: 23-09-2019 Through 25-09-2019",

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doi = "10.1145/3351995.3352035",

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N2 - Predictive analysis is an important part of data analysis. Predictive models, based on Statistics or Machine Learning, are increasingly used to estimate, with a certain probability, future values of the variables that describe a phenomenon. Different models produce different results on a same dataset; thus, several models should be compared in order to identify the most suitable one. The paper is part of a larger research that aims at providing interactive visualizations that help the analysts to compare predictive models and to select the model that best fits the data. Specifically, two visualizations are presented, which support the analysts in performing some tasks of the Keim's Visual Analytics Mantra.

AB - Predictive analysis is an important part of data analysis. Predictive models, based on Statistics or Machine Learning, are increasingly used to estimate, with a certain probability, future values of the variables that describe a phenomenon. Different models produce different results on a same dataset; thus, several models should be compared in order to identify the most suitable one. The paper is part of a larger research that aims at providing interactive visualizations that help the analysts to compare predictive models and to select the model that best fits the data. Specifically, two visualizations are presented, which support the analysts in performing some tasks of the Keim's Visual Analytics Mantra.

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KW - Pie-chart Matrix

KW - Visual Analytics

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Visual techniques to compare predictive models

Abstract

Publication series

Conference

Keywords

ASJC Scopus subject areas

Access to Document

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