XGBoost Classifier-Based Model to Predict the Nature of Gender-Based Violence. Case Study: Santander, Colombia

Gender-based violence remains a persistent social challenge in Colombia. Despite efforts to address it, statistics show a steady increase year after year. This study addresses the need for predictive solutions by introducing a Machine Learning model using XGBoost, chosen for its high performance in classification tasks with complex datasets. The model is trained on data collected from the department of Santander, Colombia, aiming to predict gender-based violence incidents based on specific socio-demographic and situational features. The motivation behind using XGBoost lies in its ability to handle diverse data types and produce accurate, interpretable results. Key influential features in the model’s predictions were identified, including the context of the incidents and the relationship between victim and the perpetrator, underscoring the importance of situational as well as individual factors. The model achieved promising results, with an accuracy, precision, recall, and F1 score exceeding 84% demonstrating its potential to effectively predict and contribute to preventing gender-based violence in the region. This approach not only represents a proactive response to a critical social challenge but also offers a framework that could be applied in similar contexts at the national and international levels.

Abstract licence: CC BY-ND

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