GMS location: 204

Random forest results

names period power significance meanSquareError absError maxError CRPS IGN
baseline winter 2016 0.983 0.194 2.451 0.870 1.324e+01 NaN NaN
forest winter 2016 0.977 0.194 2.257 0.801 1.257e+01 0.479 3.805
baseline winter 2017 0.950 0.062 0.667 0.527 5.496 NaN NaN
forest winter 2017 0.967 0.031 0.528 0.474 5.118 0.547 1.510
baseline winter 2018 0.986 0.097 0.312 0.415 1.897 NaN NaN
forest winter 2018 0.966 0.032 0.431 0.444 3.799 0.666 1.579
baseline winter 2019 0.993 0.000e+00 0.357 0.437 2.304 NaN NaN
forest winter 2019 0.993 0.111 0.270 0.387 1.801 0.603 1.554
baseline all 0.979 0.107 1.037 0.581 1.324e+01 NaN NaN
forest all 0.976 0.087 0.961 0.544 1.257e+01 0.570 2.220

Random forest plots

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Extended logistic regression results

names period power significance meanSquareError absError maxError CRPS IGN
baseline winter 2016 0.983 0.194 2.451 0.870 1.324e+01 NaN NaN
elr winter 2016 0.977 0.226 2.338 0.820 1.313e+01 0.516 3.259
baseline winter 2017 0.950 0.062 0.667 0.527 5.496 NaN NaN
elr winter 2017 0.967 0.031 0.535 0.478 5.017 0.482 1.358
baseline winter 2018 0.986 0.097 0.312 0.415 1.897 NaN NaN
elr winter 2018 0.973 0.065 0.309 0.427 2.065 0.516 1.385
baseline winter 2019 0.993 0.000e+00 0.357 0.437 2.304 NaN NaN
elr winter 2019 1.000 0.111 0.274 0.388 2.356 0.464 1.244
baseline all 0.979 0.107 1.037 0.581 1.324e+01 NaN NaN
elr all 0.979 0.107 0.955 0.547 1.313e+01 0.497 1.905

Extended logistic regression plots

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