GMS location: 202

Random forest results

names period power significance meanSquareError absError maxError CRPS IGN
baseline winter 2016 0.984 0.000e+00 0.369 0.480 1.855 NaN NaN
forest winter 2016 0.995 0.105 0.260 0.394 1.388 0.460 4.193
baseline winter 2017 0.960 0.138 0.470 0.521 2.313 NaN NaN
forest winter 2017 0.960 0.103 0.303 0.425 1.901 0.476 4.976
baseline winter 2018 0.987 0.107 0.350 0.468 1.558 NaN NaN
forest winter 2018 0.980 0.107 0.262 0.405 1.536 0.478 3.077
baseline winter 2019 0.993 0.000e+00 0.292 0.403 2.015 NaN NaN
forest winter 2019 0.993 0.083 0.234 0.358 1.735 0.468 3.492
baseline all 0.982 0.080 0.369 0.468 2.313 NaN NaN
forest all 0.984 0.102 0.264 0.395 1.901 0.470 3.916

Random forest plots

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Extended logistic regression results

names period power significance meanSquareError absError maxError CRPS IGN
baseline winter 2016 0.984 0.000e+00 0.369 0.480 1.855 NaN NaN
elr winter 2016 0.984 0.105 0.288 0.419 1.718 0.536 5.052
baseline winter 2017 0.960 0.138 0.470 0.521 2.313 NaN NaN
elr winter 2017 0.976 0.069 0.344 0.442 1.975 0.519 5.133
baseline winter 2018 0.987 0.107 0.350 0.468 1.558 NaN NaN
elr winter 2018 0.987 0.107 0.310 0.438 1.855 0.546 4.967
baseline winter 2019 0.993 0.000e+00 0.292 0.403 2.015 NaN NaN
elr winter 2019 0.993 0.083 0.261 0.380 2.228 0.526 4.815
baseline all 0.982 0.080 0.369 0.468 2.313 NaN NaN
elr all 0.985 0.091 0.300 0.420 2.228 0.533 4.994

Extended logistic regression plots

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