GMS location: 913

Random forest results

names period power significance meanSquareError absError maxError CRPS IGN
baseline winter 2016 0.988 0.000e+00 0.315 0.433 1.790 NaN NaN
forest winter 2016 0.994 0.105 0.251 0.372 1.590 0.503 3.326
baseline winter 2017 0.954 0.029 0.317 0.404 2.370 NaN NaN
forest winter 2017 0.954 0.029 0.242 0.348 2.215 0.465 3.012
baseline winter 2018 0.993 0.031 0.312 0.402 2.095 NaN NaN
forest winter 2018 0.993 0.062 0.266 0.381 2.164 0.498 3.276
baseline winter 2019 0.993 0.067 0.446 0.462 3.401 NaN NaN
forest winter 2019 0.993 0.133 0.350 0.403 3.196 0.480 4.297
baseline all 0.984 0.030 0.345 0.425 3.401 NaN NaN
forest all 0.986 0.069 0.276 0.377 3.196 0.488 3.471

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 0.000e+00 0.315 0.433 1.790 NaN NaN
elr winter 2016 0.981 0.053 0.275 0.394 1.626 0.576 4.517
baseline winter 2017 0.954 0.029 0.317 0.404 2.370 NaN NaN
elr winter 2017 0.963 0.057 0.272 0.378 2.250 0.538 4.417
baseline winter 2018 0.993 0.031 0.312 0.402 2.095 NaN NaN
elr winter 2018 0.993 0.062 0.284 0.383 2.299 0.552 4.839
baseline winter 2019 0.993 0.067 0.446 0.462 3.401 NaN NaN
elr winter 2019 0.993 0.133 0.354 0.397 3.187 0.549 5.472
baseline all 0.984 0.030 0.345 0.425 3.401 NaN NaN
elr all 0.984 0.069 0.295 0.388 3.187 0.555 4.806

Extended logistic regression plots

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