GMS location: 512

Random forest results

names period power significance meanSquareError absError maxError CRPS IGN
baseline winter 2016 0.983 0.000e+00 0.379 0.477 2.294 NaN NaN
forest winter 2016 0.994 0.062 0.250 0.372 2.207 0.459 4.931
baseline winter 2017 0.984 0.103 0.457 0.513 2.030 NaN NaN
forest winter 2017 1.000 0.103 0.254 0.379 1.534 0.443 4.267
baseline winter 2018 0.993 0.115 0.322 0.432 1.889 NaN NaN
forest winter 2018 0.993 0.154 0.230 0.353 1.778 0.447 3.869
baseline winter 2019 0.985 0.000e+00 0.281 0.391 1.500 NaN NaN
forest winter 2019 0.993 0.000e+00 0.246 0.377 1.608 0.441 3.864
baseline all 0.986 0.074 0.361 0.455 2.294 NaN NaN
forest all 0.995 0.099 0.245 0.370 2.207 0.448 4.265

Random forest plots

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Extended logistic regression results

names period power significance meanSquareError absError maxError CRPS IGN
baseline winter 2016 0.983 0.000e+00 0.379 0.477 2.294 NaN NaN
elr winter 2016 0.989 0.062 0.302 0.422 2.052 0.515 5.194
baseline winter 2017 0.984 0.103 0.457 0.513 2.030 NaN NaN
elr winter 2017 1.000 0.103 0.299 0.422 1.617 0.496 4.608
baseline winter 2018 0.993 0.115 0.322 0.432 1.889 NaN NaN
elr winter 2018 0.993 0.154 0.296 0.418 1.884 0.530 5.164
baseline winter 2019 0.985 0.000e+00 0.281 0.391 1.500 NaN NaN
elr winter 2019 0.993 0.000e+00 0.260 0.379 1.687 0.506 4.645
baseline all 0.986 0.074 0.361 0.455 2.294 NaN NaN
elr all 0.993 0.099 0.291 0.411 2.052 0.513 4.931

Extended logistic regression plots

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