GMS location: 1233

Random forest results

names period power significance meanSquareError absError maxError CRPS IGN
baseline winter 2016 0.988 0.069 0.378 0.448 2.000 NaN NaN
forest winter 2016 0.988 0.069 0.309 0.408 1.873 0.523 2.571
baseline winter 2017 0.983 0.030 0.553 0.549 2.224 NaN NaN
forest winter 2017 0.983 0.030 0.423 0.476 2.034 0.529 3.077
baseline winter 2018 0.985 0.154 0.426 0.472 2.563 NaN NaN
forest winter 2018 0.955 0.051 0.383 0.430 2.806 0.549 2.795
baseline winter 2019 0.992 0.000e+00 0.436 0.482 2.448 NaN NaN
forest winter 2019 0.992 0.000e+00 0.302 0.400 1.827 0.546 2.821
baseline all 0.987 0.079 0.444 0.485 2.563 NaN NaN
forest all 0.980 0.044 0.354 0.428 2.806 0.536 2.800

Random forest plots

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Extended logistic regression results

names period power significance meanSquareError absError maxError CRPS IGN
baseline winter 2016 0.988 0.069 0.378 0.448 2.000 NaN NaN
elr winter 2016 0.988 0.035 0.372 0.465 1.916 0.615 3.815
baseline winter 2017 0.983 0.030 0.553 0.549 2.224 NaN NaN
elr winter 2017 0.958 0.030 0.493 0.530 1.982 0.607 4.161
baseline winter 2018 0.985 0.154 0.426 0.472 2.563 NaN NaN
elr winter 2018 0.963 0.103 0.401 0.466 2.829 0.609 3.808
baseline winter 2019 0.992 0.000e+00 0.436 0.482 2.448 NaN NaN
elr winter 2019 1.000 0.000e+00 0.348 0.435 2.152 0.579 3.485
baseline all 0.987 0.079 0.444 0.485 2.563 NaN NaN
elr all 0.978 0.053 0.403 0.474 2.829 0.604 3.825

Extended logistic regression plots

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