  Gender-based crime is one of the most concerning scourges of contemporary
society. Governments worldwide have invested lots of economic and human
resources to radically eliminate this threat. Despite these efforts, providing
accurate predictions of the risk that a victim of gender violence has of being
attacked again is still a very hard open problem. The development of new
methods for issuing accurate, fair and quick predictions would allow police
forces to select the most appropriate measures to prevent recidivism. In this
work, we propose to apply Machine Learning (ML) techniques to create models
that accurately predict the recidivism risk of a gender-violence offender. The
relevance of the contribution of this work is threefold: (i) the proposed ML
method outperforms the preexisting risk assessment algorithm based on classical
statistical techniques, (ii) the study has been conducted through an official
specific-purpose database with more than 40,000 reports of gender violence, and
(iii) two new quality measures are proposed for assessing the effective police
protection that a model supplies and the overload in the invested resources
that it generates. Additionally, we propose a hybrid model that combines the
statistical prediction methods with the ML method, permitting authorities to
implement a smooth transition from the preexisting model to the ML-based model.
This hybrid nature enables a decision-making process to optimally balance
between the efficiency of the police system and aggressiveness of the
protection measures taken.
