GMS location: 474

Random forest results

names period power significance meanSquareError absError maxError CRPS IGN
baseline winter 2016 1.000 0.111 0.353 0.445 1.895 NaN NaN
forest winter 2016 1.000 0.111 0.288 0.397 1.750 0.508 2.813
baseline winter 2017 0.964 0.073 0.424 0.476 2.595 NaN NaN
forest winter 2017 0.964 0.049 0.361 0.431 2.215 0.494 3.535
baseline winter 2018 0.985 0.111 0.426 0.437 3.137 NaN NaN
forest winter 2018 0.977 0.074 0.381 0.409 3.158 0.506 3.079
baseline winter 2019 0.985 0.000e+00 0.285 0.378 2.057 NaN NaN
forest winter 2019 0.985 0.000e+00 0.231 0.339 1.540 0.492 2.378
baseline all 0.985 0.080 0.372 0.435 3.137 NaN NaN
forest all 0.984 0.060 0.315 0.395 3.158 0.500 2.949

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 0.111 0.353 0.445 1.895 NaN NaN
elr winter 2016 1.000 0.111 0.398 0.480 2.053 0.587 4.563
baseline winter 2017 0.964 0.073 0.424 0.476 2.595 NaN NaN
elr winter 2017 0.955 0.049 0.401 0.474 2.490 0.583 5.235
baseline winter 2018 0.985 0.111 0.426 0.437 3.137 NaN NaN
elr winter 2018 0.985 0.074 0.405 0.427 3.230 0.602 5.312
baseline winter 2019 0.985 0.000e+00 0.285 0.378 2.057 NaN NaN
elr winter 2019 0.985 0.000e+00 0.273 0.389 1.727 0.567 4.113
baseline all 0.985 0.080 0.372 0.435 3.137 NaN NaN
elr all 0.984 0.060 0.372 0.445 3.230 0.585 4.802

Extended logistic regression plots

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