GMS location: 718

Random forest results

names period power significance meanSquareError absError maxError CRPS IGN
baseline winter 2016 1.000 0.000e+00 0.359 0.427 2.200 NaN NaN
forest winter 2016 1.000 0.000e+00 0.318 0.403 2.223 0.488 3.009
baseline winter 2017 0.966 0.121 0.327 0.417 2.386 NaN NaN
forest winter 2017 0.966 0.091 0.255 0.378 1.910 0.483 2.768
baseline winter 2018 0.993 0.083 0.397 0.486 2.997 NaN NaN
forest winter 2018 1.000 0.083 0.338 0.441 3.082 0.483 2.861
baseline winter 2019 0.984 0.000e+00 0.292 0.390 2.354 NaN NaN
forest winter 2019 0.984 0.000e+00 0.233 0.354 1.749 0.484 2.284
baseline all 0.988 0.078 0.348 0.432 2.997 NaN NaN
forest all 0.990 0.065 0.291 0.397 3.082 0.485 2.772

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 0.000e+00 0.359 0.427 2.200 NaN NaN
elr winter 2016 1.000 0.077 0.368 0.437 2.536 0.528 4.055
baseline winter 2017 0.966 0.121 0.327 0.417 2.386 NaN NaN
elr winter 2017 0.958 0.091 0.305 0.424 2.082 0.565 4.639
baseline winter 2018 0.993 0.083 0.397 0.486 2.997 NaN NaN
elr winter 2018 1.000 0.083 0.381 0.481 3.191 0.537 4.298
baseline winter 2019 0.984 0.000e+00 0.292 0.390 2.354 NaN NaN
elr winter 2019 0.984 0.000e+00 0.241 0.367 1.735 0.501 3.082
baseline all 0.988 0.078 0.348 0.432 2.997 NaN NaN
elr all 0.988 0.078 0.331 0.431 3.191 0.534 4.062

Extended logistic regression plots

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