GMS location: 1011

Random forest results

names period power significance meanSquareError absError maxError CRPS IGN
baseline winter 2016 1.000 0.067 0.309 0.393 2.701 NaN NaN
forest winter 2016 1.000 0.267 0.301 0.409 2.640 0.430 2.670
baseline winter 2017 0.961 0.000e+00 0.439 0.466 2.881 NaN NaN
forest winter 2017 0.985 0.000e+00 0.328 0.409 2.359 0.437 2.458
baseline winter 2018 0.975 0.167 0.340 0.434 2.271 NaN NaN
forest winter 2018 0.987 0.167 0.292 0.399 2.121 0.441 2.366
baseline winter 2019 0.987 0.000e+00 0.247 0.363 1.572 NaN NaN
forest winter 2019 0.987 0.000e+00 0.190 0.323 1.525 0.429 1.986
baseline all 0.983 0.052 0.330 0.411 2.881 NaN NaN
forest all 0.991 0.103 0.278 0.386 2.640 0.434 2.386

Random forest plots

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Extended logistic regression results

names period power significance meanSquareError absError maxError CRPS IGN
baseline winter 2016 1.000 0.067 0.309 0.393 2.701 NaN NaN
elr winter 2016 1.000 0.267 0.324 0.421 2.771 0.471 3.392
baseline winter 2017 0.961 0.000e+00 0.439 0.466 2.881 NaN NaN
elr winter 2017 0.969 0.045 0.389 0.444 2.684 0.498 4.947
baseline winter 2018 0.975 0.167 0.340 0.434 2.271 NaN NaN
elr winter 2018 0.981 0.083 0.301 0.406 1.832 0.489 3.691
baseline winter 2019 0.987 0.000e+00 0.247 0.363 1.572 NaN NaN
elr winter 2019 0.981 0.000e+00 0.219 0.356 1.633 0.491 3.364
baseline all 0.983 0.052 0.330 0.411 2.881 NaN NaN
elr all 0.984 0.103 0.307 0.407 2.771 0.486 3.798

Extended logistic regression plots

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