GMS location: 212

Random forest results

names period power significance meanSquareError absError maxError CRPS IGN
baseline winter 2016 0.977 0.107 0.832 0.598 4.828 NaN NaN
forest winter 2016 0.966 0.036 0.819 0.600 4.698 0.503 1.369
baseline winter 2017 0.990 0.106 0.828 0.616 4.296 NaN NaN
forest winter 2017 0.980 0.085 0.814 0.606 4.228 0.480 1.331
baseline winter 2018 0.993 0.100 1.377 0.790 4.600 NaN NaN
forest winter 2018 0.978 0.150 1.321 0.751 4.222 0.497 1.400
baseline winter 2019 0.972 0.000e+00 0.271 0.382 1.856 NaN NaN
forest winter 2019 0.972 0.000e+00 0.310 0.402 2.135 0.540 1.409
baseline all 0.982 0.094 0.843 0.601 4.828 NaN NaN
forest all 0.973 0.086 0.830 0.595 4.698 0.505 1.378

Random forest plots

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Extended logistic regression results

names period power significance meanSquareError absError maxError CRPS IGN
baseline winter 2016 0.977 0.107 0.832 0.598 4.828 NaN NaN
elr winter 2016 0.966 0.036 0.833 0.591 4.919 0.548 1.499
baseline winter 2017 0.990 0.106 0.828 0.616 4.296 NaN NaN
elr winter 2017 0.990 0.128 0.764 0.581 3.886 0.462 1.225
baseline winter 2018 0.993 0.100 1.377 0.790 4.600 NaN NaN
elr winter 2018 0.978 0.125 1.235 0.740 4.585 0.531 1.635
baseline winter 2019 0.972 0.000e+00 0.271 0.382 1.856 NaN NaN
elr winter 2019 0.972 0.000e+00 0.329 0.435 1.879 0.539 1.364
baseline all 0.982 0.094 0.843 0.601 4.828 NaN NaN
elr all 0.975 0.094 0.806 0.591 4.919 0.523 1.444

Extended logistic regression plots

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