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Interpretable-machine-learning-in-CKD
Interpretable-machine-learning-in-CKD PublicThis paper presents a machine learning study for predicting chronic kidney disease (CKD) progression risk using clinical data
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xgboost
xgboost PublicForked from dmlc/xgboost
Scalable, Portable and Distributed Gradient Boosting (GBDT, GBRT or GBM) Library, for Python, R, Java, Scala, C++ and more. Runs on single machine, Hadoop, Spark, Flink and DataFlow
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point-into-raster
point-into-raster PublicChange the (lat and lon) point data into the special shape file (which is your own country or district) according to IDW interpolation.
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Schistosomiasis-risk-with-Rainfall
Schistosomiasis-risk-with-Rainfall PublicUsing rainfall to predict the Sch risk levels
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ebird-best-practices
ebird-best-practices PublicWorking through using auk, EBD, and habitat covariates based on ebook from https://cornelllabofornithology.github.io/ebird-best-practices/
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