GMS location: 1401

Random forest results

names period power significance meanSquareError absError maxError CRPS IGN
baseline winter 2016 0.988 0.033 0.352 0.447 2.719 NaN NaN
forest winter 2016 0.988 0.000e+00 0.322 0.429 2.362 0.542 3.516
baseline winter 2017 0.973 0.025 0.458 0.478 2.207 NaN NaN
forest winter 2017 0.955 0.025 0.392 0.455 2.031 0.531 2.990
baseline winter 2018 1.000 0.146 0.386 0.463 1.849 NaN NaN
forest winter 2018 0.991 0.098 0.300 0.415 1.513 0.522 2.881
baseline winter 2019 0.993 0.059 0.376 0.450 2.301 NaN NaN
forest winter 2019 0.993 0.000e+00 0.315 0.431 1.905 0.516 3.131
baseline all 0.988 0.070 0.390 0.459 2.719 NaN NaN
forest all 0.983 0.039 0.332 0.432 2.362 0.529 3.155

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 0.033 0.352 0.447 2.719 NaN NaN
elr winter 2016 0.982 0.000e+00 0.355 0.458 2.414 0.599 4.268
baseline winter 2017 0.973 0.025 0.458 0.478 2.207 NaN NaN
elr winter 2017 0.964 0.025 0.446 0.489 2.139 0.612 5.445
baseline winter 2018 1.000 0.146 0.386 0.463 1.849 NaN NaN
elr winter 2018 1.000 0.073 0.328 0.450 1.453 0.593 4.376
baseline winter 2019 0.993 0.059 0.376 0.450 2.301 NaN NaN
elr winter 2019 0.993 0.059 0.370 0.479 1.812 0.571 4.348
baseline all 0.988 0.070 0.390 0.459 2.719 NaN NaN
elr all 0.985 0.039 0.374 0.468 2.414 0.594 4.590

Extended logistic regression plots

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