Catatan bukti metode

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

Catatan sumber

Kutipan disalin apa adanya dari catatan sumber metode. Tidak ada verifikasi tingkat klaim yang disimpulkan darinya.

Machine Learning Methods for Predicting Armed Conflict

Catatan metode taksonomi · 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

Buka metode lengkap

Klaim yang dikurasi

Klaim tersimpan dalam buku besar bukti, masing-masing dengan penilaiannya sendiri.

Belum ada klaim yang dikurasi

Tampilan ini tidak menciptakan penilaian klaim ketika buku besar tidak memilikinya.

Metode terkait

Dihasilkan dari grafik metode dan ditampilkan sebagai relasi yang disarankan mesin — tidak ada klaim bukti yang disimpulkan.

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.