GMS location: 840

Random forest results

names period power significance meanSquareError absError maxError CRPS IGN
baseline winter 2016 0.994 0.100 0.308 0.390 2.467 NaN NaN
forest winter 2016 0.988 0.100 0.276 0.367 2.351 0.510 4.508
baseline winter 2017 0.982 0.050 0.331 0.410 2.273 NaN NaN
forest winter 2017 0.982 0.050 0.305 0.400 2.108 0.490 4.058
baseline winter 2018 0.973 0.065 0.368 0.457 2.134 NaN NaN
forest winter 2018 0.980 0.065 0.296 0.388 2.106 0.491 3.374
baseline winter 2019 0.978 0.048 0.332 0.422 1.978 NaN NaN
forest winter 2019 0.978 0.048 0.269 0.386 1.623 0.482 3.052
baseline all 0.982 0.062 0.335 0.420 2.467 NaN NaN
forest all 0.982 0.062 0.286 0.384 2.351 0.494 3.761

Random forest plots

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Extended logistic regression results

names period power significance meanSquareError absError maxError CRPS IGN
baseline winter 2016 0.994 0.100 0.308 0.390 2.467 NaN NaN
elr winter 2016 0.988 0.100 0.306 0.402 2.594 0.542 4.516
baseline winter 2017 0.982 0.050 0.331 0.410 2.273 NaN NaN
elr winter 2017 0.991 0.100 0.298 0.392 2.111 0.502 3.441
baseline winter 2018 0.973 0.065 0.368 0.457 2.134 NaN NaN
elr winter 2018 0.987 0.065 0.299 0.422 1.671 0.531 3.767
baseline winter 2019 0.978 0.048 0.332 0.422 1.978 NaN NaN
elr winter 2019 0.978 0.048 0.290 0.396 2.252 0.524 3.527
baseline all 0.982 0.062 0.335 0.420 2.467 NaN NaN
elr all 0.986 0.080 0.298 0.404 2.594 0.526 3.842

Extended logistic regression plots

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