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Listar por autor "Flach, Peter"

Mostrando ítems 1-5 de 5

A unified view of performance metrics: translating threshold choice into expected classification loss

José Hernández-Orallo; Flach, Peter; Ferri Ramírez, César (Microtome Publishing, 2012)

[EN] Many performance metrics have been introduced in the literature for the evaluation of classification performance, each of them with different origins and areas of application. These metrics include accuracy, unweighted ...
CRISP-DM Twenty Years Later: From Data Mining Processes to Data Science Trajectories

Martínez-Plumed, Fernando; Contreras-Ochando, Lidia; Ferri Ramírez, César; Hernández-Orallo, José; Kull, Meelis; Lachiche, Nicolas; Ramírez Quintana, María José; Flach, Peter (Institute of Electrical and Electronics Engineers, 2021-08-01)

[EN] CRISP-DM (CRoss-Industry Standard Process for Data Mining) has its origins in the second half of the nineties and is thus about two decades old. According to many surveys and user polls it is still thede factostandard ...
Reframing in context: A systematic approach for model reuse in machine learning

Hernández Orallo, José; Martínez Usó, Adolfo; Prudencio, Ricardo B. C.; Kull, Meelis; Flach, Peter; Ahmed, Chowdhury Farhan; Lachiche, Nicolas (IOS Press, 2016)

We describe a systematic approach called reframing, defined as the process of preparing a machine learning model (e.g., a classifier) to perform well over a range of operating contexts. One way to achieve this is by ...
Report of the First International Workshop on Learning over Multiple Contexts (LMCE 2014)

Ferri Ramírez, César; Flach, Peter; Lachiche, Nicolas (ACM, 2015)

The first international workshop on Learning over Multiple Contexts, devoted to generalization and reuse of machine learning models over multiple contexts, was held on September 19th, 2014, as part of the 7th European ...
Setting decision thresholds when operating conditions are uncertain

Ferri Ramírez, César; Hernández-Orallo, José; Flach, Peter (Springer-Verlag, 2019-07)

[EN] The quality of the decisions made by a machine learning model depends on the data and the operating conditions during deployment. Often, operating conditions such as class distribution and misclassification costs have ...