GMS location: 1156

Random forest results

names period power significance meanSquareError absError maxError CRPS IGN
baseline winter 2016 1.000 0.033 0.339 0.423 2.163 NaN NaN
forest winter 2016 0.981 0.033 0.275 0.389 1.937 0.563 3.803
baseline winter 2017 0.991 0.028 0.421 0.469 1.900 NaN NaN
forest winter 2017 0.983 0.028 0.337 0.425 1.645 0.540 4.000
baseline winter 2018 0.979 0.171 0.348 0.430 2.123 NaN NaN
forest winter 2018 0.993 0.146 0.305 0.418 1.994 0.565 3.592
baseline winter 2019 0.993 0.000e+00 0.446 0.487 2.033 NaN NaN
forest winter 2019 1.000 0.000e+00 0.315 0.415 2.021 0.577 4.309
baseline all 0.991 0.076 0.384 0.450 2.163 NaN NaN
forest all 0.989 0.067 0.306 0.411 2.021 0.562 3.905

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 0.033 0.339 0.423 2.163 NaN NaN
elr winter 2016 0.994 0.000e+00 0.269 0.402 1.925 0.642 4.533
baseline winter 2017 0.991 0.028 0.421 0.469 1.900 NaN NaN
elr winter 2017 0.991 0.028 0.391 0.452 1.987 0.576 4.064
baseline winter 2018 0.979 0.171 0.348 0.430 2.123 NaN NaN
elr winter 2018 0.979 0.122 0.347 0.450 1.853 0.644 5.268
baseline winter 2019 0.993 0.000e+00 0.446 0.487 2.033 NaN NaN
elr winter 2019 0.993 0.000e+00 0.361 0.453 1.837 0.587 4.120
baseline all 0.991 0.076 0.384 0.450 2.163 NaN NaN
elr all 0.989 0.050 0.338 0.438 1.987 0.616 4.534

Extended logistic regression plots

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