GMS location: 1434

Random forest results

names period power significance meanSquareError absError maxError CRPS IGN
baseline winter 2016 0.988 0.074 0.297 0.378 2.300 NaN NaN
forest winter 2016 0.963 0.037 0.265 0.366 2.000 0.519 6.034
baseline winter 2017 0.981 0.000e+00 0.320 0.405 2.267 NaN NaN
forest winter 2017 0.981 0.000e+00 0.289 0.399 1.853 0.495 4.037
baseline winter 2018 1.000 0.103 0.329 0.431 1.601 NaN NaN
forest winter 2018 0.993 0.103 0.285 0.412 1.414 0.502 4.082
baseline winter 2019 0.985 0.000e+00 0.323 0.382 2.041 NaN NaN
forest winter 2019 0.993 0.136 0.291 0.369 2.066 0.513 3.951
baseline all 0.989 0.045 0.316 0.399 2.300 NaN NaN
forest all 0.982 0.061 0.282 0.386 2.066 0.508 4.587

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 0.074 0.297 0.378 2.300 NaN NaN
elr winter 2016 0.963 0.000e+00 0.288 0.400 1.951 0.570 4.456
baseline winter 2017 0.981 0.000e+00 0.320 0.405 2.267 NaN NaN
elr winter 2017 0.972 0.000e+00 0.298 0.413 2.222 0.583 4.425
baseline winter 2018 1.000 0.103 0.329 0.431 1.601 NaN NaN
elr winter 2018 0.979 0.103 0.297 0.432 1.350 0.567 4.705
baseline winter 2019 0.985 0.000e+00 0.323 0.382 2.041 NaN NaN
elr winter 2019 0.985 0.091 0.328 0.418 2.135 0.566 4.676
baseline all 0.989 0.045 0.316 0.399 2.300 NaN NaN
elr all 0.974 0.045 0.302 0.415 2.222 0.571 4.567

Extended logistic regression plots

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