GMS location: 1157

Random forest results

names period power significance meanSquareError absError maxError CRPS IGN
baseline winter 2016 0.994 0.000e+00 0.317 0.411 2.418 NaN NaN
forest winter 2016 0.989 0.000e+00 0.263 0.378 1.977 0.524 2.825
baseline winter 2017 0.973 0.027 0.543 0.529 2.312 NaN NaN
forest winter 2017 0.973 0.027 0.417 0.463 2.092 0.509 3.929
baseline winter 2018 0.986 0.133 0.312 0.410 2.300 NaN NaN
forest winter 2018 0.993 0.133 0.263 0.382 2.119 0.516 2.653
baseline winter 2019 0.993 0.091 0.440 0.422 4.495 NaN NaN
forest winter 2019 0.993 0.091 0.360 0.393 4.423 0.541 3.785
baseline all 0.988 0.054 0.392 0.439 4.495 NaN NaN
forest all 0.988 0.054 0.318 0.401 4.423 0.522 3.235

Random forest plots

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Extended logistic regression results

names period power significance meanSquareError absError maxError CRPS IGN
baseline winter 2016 0.994 0.000e+00 0.317 0.411 2.418 NaN NaN
elr winter 2016 0.989 0.000e+00 0.308 0.427 2.087 0.626 4.191
baseline winter 2017 0.973 0.027 0.543 0.529 2.312 NaN NaN
elr winter 2017 0.982 0.027 0.448 0.495 1.826 0.575 4.380
baseline winter 2018 0.986 0.133 0.312 0.410 2.300 NaN NaN
elr winter 2018 0.993 0.133 0.306 0.445 1.947 0.578 3.770
baseline winter 2019 0.993 0.091 0.440 0.422 4.495 NaN NaN
elr winter 2019 0.993 0.091 0.403 0.427 4.385 0.560 3.846
baseline all 0.988 0.054 0.392 0.439 4.495 NaN NaN
elr all 0.990 0.054 0.359 0.446 4.385 0.588 4.047

Extended logistic regression plots

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