GMS location: 522

Random forest results

names period power significance meanSquareError absError maxError CRPS IGN
baseline winter 2016 0.988 0.000e+00 0.577 0.493 4.795 NaN NaN
forest winter 2016 0.994 0.000e+00 0.503 0.442 4.842 0.470 2.346
baseline winter 2017 0.958 0.067 0.421 0.468 2.854 NaN NaN
forest winter 2017 0.983 0.033 0.340 0.415 2.352 0.456 1.470
baseline winter 2018 1.000 0.074 0.376 0.455 2.233 NaN NaN
forest winter 2018 1.000 0.074 0.350 0.449 2.021 0.478 1.839
baseline winter 2019 0.987 0.000e+00 0.354 0.405 3.298 NaN NaN
forest winter 2019 0.993 0.167 0.304 0.378 3.125 0.464 1.751
baseline all 0.984 0.048 0.437 0.457 4.795 NaN NaN
forest all 0.993 0.048 0.379 0.423 4.842 0.468 1.874

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 0.000e+00 0.577 0.493 4.795 NaN NaN
elr winter 2016 0.988 0.000e+00 0.538 0.463 5.362 0.541 3.665
baseline winter 2017 0.958 0.067 0.421 0.468 2.854 NaN NaN
elr winter 2017 0.967 0.033 0.359 0.447 2.443 0.511 2.390
baseline winter 2018 1.000 0.074 0.376 0.455 2.233 NaN NaN
elr winter 2018 1.000 0.074 0.374 0.480 2.221 0.556 2.943
baseline winter 2019 0.987 0.000e+00 0.354 0.405 3.298 NaN NaN
elr winter 2019 1.000 0.167 0.350 0.432 3.045 0.511 2.426
baseline all 0.984 0.048 0.437 0.457 4.795 NaN NaN
elr all 0.990 0.048 0.410 0.457 5.362 0.531 2.894

Extended logistic regression plots

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