Titlebook: Identifying the Complex Causes of Civil War; A Machine Learning A Atin Basuchoudhary,James T. Bang,Tinni Sen Book 2021 The Editor(s) (if ap

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Atin Basuchoudhary,James T. Bang,John David,Tinni Sen with it the following disadvantages. By drilling the holes required for the screws, the esophagus, vessels, dura or even the spinal cord may be damaged; the plates may loosen and cause injury to the esophagus; and implantation of foreign bodies increases the risk of infection.

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Data Description and Preliminary Processing,th insufficient data and then explain the process of imputing missing values in the remaining data set. Finally, we describe the process of Recursive Feature Elimination that results in the optimal number of variables that predict civil conflict.

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Infant Mortality, State Capacity, Rents, and Civil War,s of conflict. Thus, our algorithmic approach allows interpretation and moves machine learning away from its “black-box” reputation. Further, we suggest that traditional parametric empirical techniques can be misleading and have dangerous policy implications. To be precise, we show that increased go

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Foreign Aid and Civil Conflict,ome increases conflict risk only for a specific range for that variable. Our finding suggests that there should be greater theoretical scrutiny for why only a small range of values for net secondary income increases the risk of conflict. We believe a game-theoretic approach models the interaction be

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s affected by sub axial cervical spine injury although the standardization and uniformity in approaching the traumatized patient is still lacking. The ideal classification should be accurate in establishing the severity of the lesion, useful to guide the indication for treatment, but reliable and re

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