GMS location: 557

Random forest results

names period power significance meanSquareError absError maxError CRPS IGN
baseline winter 2016 0.978 0.000e+00 0.353 0.467 1.817 NaN NaN
forest winter 2016 0.989 0.000e+00 0.242 0.383 1.581 0.432 2.748
baseline winter 2017 0.968 0.000e+00 0.453 0.515 2.099 NaN NaN
forest winter 2017 0.976 0.000e+00 0.292 0.404 1.795 0.443 3.354
baseline winter 2018 0.985 0.115 0.378 0.478 2.081 NaN NaN
forest winter 2018 0.985 0.115 0.302 0.405 1.886 0.443 2.852
baseline winter 2019 0.978 0.000e+00 0.343 0.436 1.957 NaN NaN
forest winter 2019 0.986 0.000e+00 0.286 0.399 1.863 0.442 2.751
baseline all 0.978 0.037 0.380 0.474 2.099 NaN NaN
forest all 0.985 0.037 0.278 0.397 1.886 0.439 2.914

Random forest plots

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Extended logistic regression results

names period power significance meanSquareError absError maxError CRPS IGN
baseline winter 2016 0.978 0.000e+00 0.353 0.467 1.817 NaN NaN
elr winter 2016 0.995 0.000e+00 0.269 0.409 1.742 0.496 3.842
baseline winter 2017 0.968 0.000e+00 0.453 0.515 2.099 NaN NaN
elr winter 2017 0.976 0.000e+00 0.359 0.451 1.823 0.498 4.564
baseline winter 2018 0.985 0.115 0.378 0.478 2.081 NaN NaN
elr winter 2018 0.985 0.077 0.305 0.412 2.031 0.512 5.045
baseline winter 2019 0.978 0.000e+00 0.343 0.436 1.957 NaN NaN
elr winter 2019 0.986 0.000e+00 0.340 0.453 2.122 0.533 5.183
baseline all 0.978 0.037 0.380 0.474 2.099 NaN NaN
elr all 0.986 0.025 0.315 0.429 2.122 0.509 4.604

Extended logistic regression plots

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