Record di evidenza del metodo

Machine Learning Conflict Prediction

Machine learning conflict prediction uses flexible supervised algorithms — random forests, gradient boosting, neural networks, regularized regression — to forecast the onset of armed conflict from large sets of features, prioritizing out-of-sample predictive accuracy over coefficient interpretation. Muchlinski, Siroky, He, and Kocher (2016) showed that random forests substantially outperform logistic regression at predicting class-imbalanced civil-war onset, catalyzing a shift in conflict research toward algorithmic prediction, rigorous out-of-sample validation, and the recognition that explanation and prediction are distinct goals.

Sources recorded, not reviewed

Record di origine

Citazioni copiate testualmente dal record di origine del metodo. Non si inferisce alcuna verifica a livello di affermazione da esse.

Machine Learning Methods for Predicting Armed Conflict

Record tassonomico del metodo · ml-model / international-relations

Muchlinski, D., Siroky, D., He, J., & Kocher, M. (2016). Comparing random forest with logistic regression for predicting class-imbalanced civil war onset data. Political Analysis, 24(1), 87–103. · DOI 10.1093/pan/mpv024

Apri metodo completo

Affermazioni curate

Affermazioni persistite nel registro delle evidenze, ciascuna con la propria valutazione.

Nessuna affermazione curata ancora

Questa vista non inventa una valutazione dell'affermazione quando il registro non ne ha.

Metodi correlati

Generato dal grafo dei metodi e mostrato come relazioni suggerite dalla macchina — nessuna affermazione di evidenza viene inferita.

Same method familyConflict Forecastingmachine-suggested · Relational suggestion, not evidence.Used in the same domainDyadic Conflict Analysismachine-suggested · Relational suggestion, not evidence.See alsoLogistic Regressionmachine-suggested · Relational suggestion, not evidence.