GMS location: 1175

Random forest results

names period power significance meanSquareError absError maxError CRPS IGN
baseline winter 2016 0.989 0.000e+00 0.337 0.420 2.282 NaN NaN
forest winter 2016 0.983 0.000e+00 0.278 0.386 1.808 0.529 2.965
baseline winter 2017 0.983 0.026 0.369 0.445 1.815 NaN NaN
forest winter 2017 0.983 0.026 0.290 0.397 1.626 0.518 4.227
baseline winter 2018 0.978 0.125 0.379 0.436 2.033 NaN NaN
forest winter 2018 0.986 0.125 0.323 0.425 1.681 0.537 3.680
baseline winter 2019 0.993 0.000e+00 0.384 0.406 3.084 NaN NaN
forest winter 2019 0.993 0.000e+00 0.297 0.383 2.729 0.520 3.845
baseline all 0.986 0.043 0.365 0.426 3.084 NaN NaN
forest all 0.986 0.043 0.296 0.397 2.729 0.526 3.623

Random forest plots

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Extended logistic regression results

names period power significance meanSquareError absError maxError CRPS IGN
baseline winter 2016 0.989 0.000e+00 0.337 0.420 2.282 NaN NaN
elr winter 2016 0.983 0.000e+00 0.339 0.458 1.982 0.618 5.646
baseline winter 2017 0.983 0.026 0.369 0.445 1.815 NaN NaN
elr winter 2017 0.983 0.026 0.317 0.412 1.867 0.537 3.543
baseline winter 2018 0.978 0.125 0.379 0.436 2.033 NaN NaN
elr winter 2018 0.986 0.156 0.344 0.455 1.809 0.610 5.175
baseline winter 2019 0.993 0.000e+00 0.384 0.406 3.084 NaN NaN
elr winter 2019 0.993 0.000e+00 0.373 0.438 2.901 0.531 3.914
baseline all 0.986 0.043 0.365 0.426 3.084 NaN NaN
elr all 0.986 0.052 0.343 0.442 2.901 0.579 4.669

Extended logistic regression plots

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