GMS location: 872

Random forest results

names period power significance meanSquareError absError maxError CRPS IGN
baseline winter 2016 0.966 0.125 0.357 0.425 2.639 NaN NaN
forest winter 2016 0.977 0.125 0.289 0.378 2.531 0.473 4.831
baseline winter 2017 0.960 0.148 0.406 0.457 2.621 NaN NaN
forest winter 2017 0.984 0.148 0.286 0.378 1.906 0.452 3.667
baseline winter 2018 0.993 0.191 0.294 0.409 2.000 NaN NaN
forest winter 2018 0.993 0.191 0.259 0.378 2.251 0.452 3.369
baseline winter 2019 0.986 0.000e+00 0.297 0.404 2.035 NaN NaN
forest winter 2019 0.993 0.071 0.201 0.325 1.502 0.438 2.875
baseline all 0.976 0.128 0.338 0.423 2.639 NaN NaN
forest all 0.987 0.141 0.260 0.366 2.531 0.455 3.739

Random forest plots

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Extended logistic regression results

names period power significance meanSquareError absError maxError CRPS IGN
baseline winter 2016 0.966 0.125 0.357 0.425 2.639 NaN NaN
elr winter 2016 0.966 0.062 0.314 0.420 2.430 0.552 7.022
baseline winter 2017 0.960 0.148 0.406 0.457 2.621 NaN NaN
elr winter 2017 0.984 0.111 0.286 0.383 1.901 0.505 5.195
baseline winter 2018 0.993 0.191 0.294 0.409 2.000 NaN NaN
elr winter 2018 0.993 0.191 0.241 0.354 2.078 0.522 5.009
baseline winter 2019 0.986 0.000e+00 0.297 0.404 2.035 NaN NaN
elr winter 2019 1.000 0.000e+00 0.185 0.312 1.287 0.484 4.023
baseline all 0.976 0.128 0.338 0.423 2.639 NaN NaN
elr all 0.985 0.103 0.259 0.369 2.430 0.518 5.396

Extended logistic regression plots

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