As a result of this joint learning, splits in the random forest are more likely to occur along informative genetic features that are orthogonal (that is, not correlated) to population structure.

ProPublica borrowed machine learning methods from academic research to better understand links between forest loss and spillover risk. The results were surprising, but led us to a story we wouldn ...