GMS location: 535

Random forest results

names period power significance meanSquareError absError maxError CRPS IGN
baseline winter 2016 0.995 0.000e+00 0.375 0.459 1.902 NaN NaN
forest winter 2016 0.990 0.077 0.206 0.344 1.402 0.678 6.143
baseline winter 2017 0.992 0.080 0.358 0.447 1.948 NaN NaN
forest winter 2017 0.992 0.080 0.295 0.382 1.919 0.649 7.641
baseline winter 2018 0.993 0.095 0.423 0.466 1.971 NaN NaN
forest winter 2018 0.993 0.095 0.341 0.415 2.220 0.701 7.674
baseline winter 2019 0.985 0.000e+00 0.283 0.402 2.149 NaN NaN
forest winter 2019 0.985 0.000e+00 0.164 0.287 1.659 0.684 5.892
baseline all 0.992 0.059 0.363 0.446 2.149 NaN NaN
forest all 0.990 0.073 0.251 0.358 2.220 0.678 6.811

Random forest plots

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Extended logistic regression results

names period power significance meanSquareError absError maxError CRPS IGN
baseline winter 2016 0.995 0.000e+00 0.375 0.459 1.902 NaN NaN
elr winter 2016 0.985 0.000e+00 0.235 0.375 1.457 0.786 1.149e+01
baseline winter 2017 0.992 0.080 0.358 0.447 1.948 NaN NaN
elr winter 2017 0.992 0.040 0.310 0.394 1.954 0.694 1.225e+01
baseline winter 2018 0.993 0.095 0.423 0.466 1.971 NaN NaN
elr winter 2018 0.993 0.095 0.367 0.434 2.363 0.790 1.330e+01
baseline winter 2019 0.985 0.000e+00 0.283 0.402 2.149 NaN NaN
elr winter 2019 0.985 0.000e+00 0.170 0.309 1.391 0.766 1.069e+01
baseline all 0.992 0.059 0.363 0.446 2.149 NaN NaN
elr all 0.988 0.044 0.271 0.380 2.363 0.762 1.194e+01

Extended logistic regression plots

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