GMS location: 1408

Random forest results

names period power significance meanSquareError absError maxError CRPS IGN
baseline winter 2016 0.989 0.103 0.344 0.425 2.788 NaN NaN
forest winter 2016 0.989 0.069 0.318 0.417 2.596 0.480 3.479
baseline winter 2017 0.973 0.105 0.481 0.488 2.977 NaN NaN
forest winter 2017 0.982 0.053 0.442 0.463 2.310 0.490 3.963
baseline winter 2018 1.000 0.105 0.356 0.435 2.668 NaN NaN
forest winter 2018 0.986 0.079 0.336 0.432 2.590 0.510 3.493
baseline winter 2019 0.986 0.000e+00 0.331 0.427 1.698 NaN NaN
forest winter 2019 0.971 0.000e+00 0.282 0.407 1.591 0.503 3.394
baseline all 0.988 0.093 0.374 0.442 2.977 NaN NaN
forest all 0.982 0.059 0.341 0.429 2.596 0.495 3.569

Random forest plots

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Extended logistic regression results

names period power significance meanSquareError absError maxError CRPS IGN
baseline winter 2016 0.989 0.103 0.344 0.425 2.788 NaN NaN
elr winter 2016 0.994 0.103 0.313 0.422 2.352 0.544 4.303
baseline winter 2017 0.973 0.105 0.481 0.488 2.977 NaN NaN
elr winter 2017 0.991 0.079 0.423 0.451 2.704 0.494 3.997
baseline winter 2018 1.000 0.105 0.356 0.435 2.668 NaN NaN
elr winter 2018 0.993 0.079 0.421 0.494 2.988 0.600 5.396
baseline winter 2019 0.986 0.000e+00 0.331 0.427 1.698 NaN NaN
elr winter 2019 0.986 0.000e+00 0.282 0.402 1.497 0.608 4.811
baseline all 0.988 0.093 0.374 0.442 2.977 NaN NaN
elr all 0.991 0.076 0.358 0.443 2.988 0.562 4.634

Extended logistic regression plots

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