GMS location: 563

Random forest results

names period power significance meanSquareError absError maxError CRPS IGN
baseline winter 2016 0.989 0.000e+00 0.356 0.450 2.199 NaN NaN
forest winter 2016 0.989 0.053 0.311 0.421 1.979 0.507 5.047
baseline winter 2017 0.977 0.051 0.338 0.424 2.071 NaN NaN
forest winter 2017 0.989 0.077 0.277 0.394 1.755 0.506 4.239
baseline winter 2018 1.000 0.094 0.272 0.407 1.626 NaN NaN
forest winter 2018 0.993 0.031 0.216 0.364 1.398 0.508 2.881
baseline winter 2019 0.992 0.000e+00 0.312 0.410 2.004 NaN NaN
forest winter 2019 0.992 0.000e+00 0.256 0.364 1.702 0.492 3.737
baseline all 0.991 0.051 0.320 0.424 2.199 NaN NaN
forest all 0.991 0.051 0.266 0.388 1.979 0.504 4.013

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 0.356 0.450 2.199 NaN NaN
elr winter 2016 0.989 0.053 0.334 0.457 1.943 0.553 5.195
baseline winter 2017 0.977 0.051 0.338 0.424 2.071 NaN NaN
elr winter 2017 0.989 0.051 0.297 0.415 1.691 0.570 4.670
baseline winter 2018 1.000 0.094 0.272 0.407 1.626 NaN NaN
elr winter 2018 0.993 0.062 0.250 0.396 1.398 0.564 4.028
baseline winter 2019 0.992 0.000e+00 0.312 0.410 2.004 NaN NaN
elr winter 2019 0.992 0.000e+00 0.276 0.390 1.755 0.535 4.036
baseline all 0.991 0.051 0.320 0.424 2.199 NaN NaN
elr all 0.991 0.051 0.291 0.418 1.943 0.555 4.521

Extended logistic regression plots

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