GMS location: 1106

Random forest results

names period power significance meanSquareError absError maxError CRPS IGN
baseline winter 2016 0.983 0.100 0.468 0.471 3.497 NaN NaN
forest winter 2016 0.972 0.033 0.464 0.470 3.396 0.470 3.056
baseline winter 2017 0.991 0.043 0.425 0.458 2.306 NaN NaN
forest winter 2017 0.981 0.022 0.358 0.422 2.163 0.479 1.916
baseline winter 2018 1.000 NaN 0.205 0.341 1.283 NaN NaN
forest winter 2018 1.000 NaN 0.333 0.471 1.366 0.535 2.041
baseline winter 2019 1.000 0.000e+00 0.320 0.444 1.434 NaN NaN
forest winter 2019 1.000 0.000e+00 0.244 0.360 1.392 0.503 2.039
baseline all 0.988 0.061 0.425 0.457 3.497 NaN NaN
forest all 0.979 0.024 0.398 0.442 3.396 0.480 2.495

Random forest plots

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Extended logistic regression results

names period power significance meanSquareError absError maxError CRPS IGN
baseline winter 2016 0.983 0.100 0.468 0.471 3.497 NaN NaN
elr winter 2016 0.955 0.033 0.549 0.570 3.301 0.668 5.365
baseline winter 2017 0.991 0.043 0.425 0.458 2.306 NaN NaN
elr winter 2017 0.981 0.043 0.413 0.474 2.088 0.585 3.864
baseline winter 2018 1.000 NaN 0.205 0.341 1.283 NaN NaN
elr winter 2018 1.000 NaN 0.467 0.604 1.472 0.719 5.001
baseline winter 2019 1.000 0.000e+00 0.320 0.444 1.434 NaN NaN
elr winter 2019 1.000 0.000e+00 0.253 0.426 1.021 0.603 3.323
baseline all 0.988 0.061 0.425 0.457 3.497 NaN NaN
elr all 0.971 0.037 0.467 0.523 3.301 0.634 4.606

Extended logistic regression plots

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