GMS location: 501

Random forest results

names period power significance meanSquareError absError maxError CRPS IGN
baseline winter 2016 0.984 0.000e+00 0.309 0.424 1.860 NaN NaN
forest winter 2016 0.995 0.000e+00 0.246 0.377 1.887 0.511 4.437
baseline winter 2017 0.933 0.125 0.374 0.456 1.713 NaN NaN
forest winter 2017 0.950 0.094 0.287 0.404 1.435 0.491 5.121
baseline winter 2018 0.992 0.061 0.354 0.434 2.253 NaN NaN
forest winter 2018 1.000 0.061 0.279 0.394 2.295 0.508 4.135
baseline winter 2019 0.986 0.000e+00 0.284 0.391 1.847 NaN NaN
forest winter 2019 0.986 0.091 0.243 0.363 1.829 0.507 4.524
baseline all 0.976 0.063 0.329 0.426 2.253 NaN NaN
forest all 0.984 0.063 0.263 0.384 2.295 0.505 4.537

Random forest plots

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Extended logistic regression results

names period power significance meanSquareError absError maxError CRPS IGN
baseline winter 2016 0.984 0.000e+00 0.309 0.424 1.860 NaN NaN
elr winter 2016 0.989 0.000e+00 0.275 0.413 1.816 0.582 5.364
baseline winter 2017 0.933 0.125 0.374 0.456 1.713 NaN NaN
elr winter 2017 0.958 0.125 0.284 0.405 1.589 0.512 4.342
baseline winter 2018 0.992 0.061 0.354 0.434 2.253 NaN NaN
elr winter 2018 1.000 0.061 0.282 0.401 2.261 0.576 5.209
baseline winter 2019 0.986 0.000e+00 0.284 0.391 1.847 NaN NaN
elr winter 2019 0.986 0.091 0.285 0.407 2.016 0.559 4.655
baseline all 0.976 0.063 0.329 0.426 2.253 NaN NaN
elr all 0.984 0.074 0.281 0.407 2.261 0.559 4.934

Extended logistic regression plots

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