GMS location: 1402

Random forest results

names period power significance meanSquareError absError maxError CRPS IGN
baseline winter 2016 1.000 0.057 0.319 0.428 3.000 NaN NaN
forest winter 2016 0.974 0.000e+00 0.289 0.402 2.650 0.519 3.324
baseline winter 2017 0.973 0.046 0.407 0.450 2.695 NaN NaN
forest winter 2017 0.954 0.070 0.359 0.442 2.192 0.502 2.937
baseline winter 2018 0.992 0.118 0.427 0.490 1.701 NaN NaN
forest winter 2018 0.985 0.118 0.364 0.474 1.791 0.528 3.209
baseline winter 2019 0.985 0.000e+00 0.421 0.468 3.302 NaN NaN
forest winter 2019 0.985 0.115 0.347 0.436 3.039 0.491 3.148
baseline all 0.989 0.058 0.390 0.458 3.302 NaN NaN
forest all 0.976 0.072 0.338 0.437 3.039 0.510 3.165

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 0.057 0.319 0.428 3.000 NaN NaN
elr winter 2016 0.981 0.000e+00 0.324 0.418 2.807 0.534 3.138
baseline winter 2017 0.973 0.046 0.407 0.450 2.695 NaN NaN
elr winter 2017 0.973 0.046 0.378 0.447 2.444 0.539 3.564
baseline winter 2018 0.992 0.118 0.427 0.490 1.701 NaN NaN
elr winter 2018 0.992 0.088 0.356 0.442 1.655 0.593 4.295
baseline winter 2019 0.985 0.000e+00 0.421 0.468 3.302 NaN NaN
elr winter 2019 0.971 0.115 0.326 0.424 2.855 0.542 3.341
baseline all 0.989 0.058 0.390 0.458 3.302 NaN NaN
elr all 0.979 0.058 0.344 0.432 2.855 0.552 3.568

Extended logistic regression plots

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