GMS location: 964

Random forest results

names period power significance meanSquareError absError maxError CRPS IGN
baseline winter 2016 0.987 0.038 2.396 0.720 1.047e+01 NaN NaN
forest winter 2016 1.000 0.115 2.426 0.705 1.032e+01 0.460 5.203
baseline winter 2017 1.000 0.023 0.344 0.401 2.633 NaN NaN
forest winter 2017 1.000 0.046 0.274 0.355 2.108 0.443 2.474
baseline winter 2018 0.986 0.038 0.398 0.460 2.152 NaN NaN
forest winter 2018 0.966 0.000e+00 0.507 0.501 2.838 0.612 2.836
baseline winter 2019 0.971 0.048 0.291 0.389 2.215 NaN NaN
forest winter 2019 0.978 0.048 0.196 0.327 1.614 0.519 2.275
baseline all 0.985 0.035 0.909 0.501 1.047e+01 NaN NaN
forest all 0.985 0.052 0.907 0.483 1.032e+01 0.509 3.270

Random forest plots

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Extended logistic regression results

names period power significance meanSquareError absError maxError CRPS IGN
baseline winter 2016 0.987 0.038 2.396 0.720 1.047e+01 NaN NaN
elr winter 2016 1.000 0.077 2.480 0.703 1.037e+01 0.535 5.013
baseline winter 2017 1.000 0.023 0.344 0.401 2.633 NaN NaN
elr winter 2017 1.000 0.070 0.394 0.456 2.656 0.505 1.720
baseline winter 2018 0.986 0.038 0.398 0.460 2.152 NaN NaN
elr winter 2018 0.986 0.077 0.461 0.507 2.150 0.537 1.945
baseline winter 2019 0.971 0.048 0.291 0.389 2.215 NaN NaN
elr winter 2019 0.978 0.143 0.299 0.415 1.766 0.483 1.493
baseline all 0.985 0.035 0.909 0.501 1.047e+01 NaN NaN
elr all 0.991 0.086 0.962 0.527 1.037e+01 0.516 2.630

Extended logistic regression plots

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