During the latest years, Big Data appears as one of the most innovative and growing scientific areas of interest. In this field, finding reliable methods to make accurate predictions represents one of the most inspirational challenges. In the following paper, the use of ROC (Receiver Operating Characteristic) Curve, a binary tool, often used for medical tests, has been used to make predictions. In particular, the attention is focused on the implementation of the ROC Curve in GAMLSS (Generalized Additive Models for Location Scale and Shape), semi-parametric models suitable for huge and flexible datasets.
Marletta, A. (2021). ROC Curve in GAMLSS as Prediction Tool for Big Data. In P. Mariani, M. Zenga (a cura di), Data Science and Social Research II : Methods, Technologies and Applications (pp. 247-257). Springer Science and Business Media Deutschland GmbH [10.1007/978-3-030-51222-4_19].
ROC Curve in GAMLSS as Prediction Tool for Big Data
Marletta, A
2021
Abstract
During the latest years, Big Data appears as one of the most innovative and growing scientific areas of interest. In this field, finding reliable methods to make accurate predictions represents one of the most inspirational challenges. In the following paper, the use of ROC (Receiver Operating Characteristic) Curve, a binary tool, often used for medical tests, has been used to make predictions. In particular, the attention is focused on the implementation of the ROC Curve in GAMLSS (Generalized Additive Models for Location Scale and Shape), semi-parametric models suitable for huge and flexible datasets.
| File | Dimensione | Formato | |
|---|---|---|---|
|
Chapter19_Marletta.pdf
Solo gestori archivio
Tipologia di allegato:
Publisher’s Version (Version of Record, VoR)
Dimensione
315.74 kB
Formato
Adobe PDF
|
315.74 kB | Adobe PDF | Visualizza/Apri Richiedi una copia |
I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
