GMS location: 1215

Random forest results

names period power significance meanSquareError absError maxError CRPS IGN
baseline winter 2016 0.969 0.100 0.367 0.444 2.628 NaN NaN
forest winter 2016 0.963 0.000e+00 0.290 0.398 2.434 0.542 2.741
baseline winter 2017 0.992 0.056 0.609 0.571 2.356 NaN NaN
forest winter 2017 1.000 0.056 0.475 0.508 2.450 0.516 3.136
baseline winter 2018 0.993 0.105 0.446 0.490 2.468 NaN NaN
forest winter 2018 0.979 0.079 0.404 0.470 2.354 0.540 2.493
baseline winter 2019 0.987 0.000e+00 0.331 0.410 2.311 NaN NaN
forest winter 2019 0.981 0.000e+00 0.286 0.399 2.179 0.537 2.497
baseline all 0.984 0.078 0.432 0.476 2.628 NaN NaN
forest all 0.979 0.043 0.360 0.441 2.450 0.534 2.704

Random forest plots

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Extended logistic regression results

names period power significance meanSquareError absError maxError CRPS IGN
baseline winter 2016 0.969 0.100 0.367 0.444 2.628 NaN NaN
elr winter 2016 0.963 0.033 0.332 0.448 2.413 0.642 3.375
baseline winter 2017 0.992 0.056 0.609 0.571 2.356 NaN NaN
elr winter 2017 1.000 0.056 0.478 0.521 2.346 0.577 3.735
baseline winter 2018 0.993 0.105 0.446 0.490 2.468 NaN NaN
elr winter 2018 0.979 0.105 0.422 0.501 2.359 0.625 3.791
baseline winter 2019 0.987 0.000e+00 0.331 0.410 2.311 NaN NaN
elr winter 2019 0.981 0.000e+00 0.340 0.458 2.097 0.597 3.145
baseline all 0.984 0.078 0.432 0.476 2.628 NaN NaN
elr all 0.979 0.060 0.390 0.480 2.413 0.612 3.508

Extended logistic regression plots

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