GMS location: 1163

Random forest results

names period power significance meanSquareError absError maxError CRPS IGN
baseline winter 2016 0.993 0.000e+00 0.378 0.435 2.439 NaN NaN
forest winter 2016 0.967 0.000e+00 0.319 0.402 1.964 0.575 2.967
baseline winter 2017 0.991 0.073 0.515 0.492 3.975 NaN NaN
forest winter 2017 0.973 0.024 0.434 0.454 3.669 0.572 3.324
baseline winter 2018 0.972 0.171 0.418 0.451 2.001 NaN NaN
forest winter 2018 0.972 0.146 0.394 0.468 2.090 0.587 2.965
baseline winter 2019 0.992 0.150 0.389 0.442 2.857 NaN NaN
forest winter 2019 0.985 0.100 0.286 0.379 2.502 0.571 2.950
baseline all 0.987 0.096 0.423 0.454 3.975 NaN NaN
forest all 0.974 0.067 0.358 0.426 3.669 0.577 3.044

Random forest plots

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Extended logistic regression results

names period power significance meanSquareError absError maxError CRPS IGN
baseline winter 2016 0.993 0.000e+00 0.378 0.435 2.439 NaN NaN
elr winter 2016 0.987 0.000e+00 0.321 0.443 2.165 0.636 3.918
baseline winter 2017 0.991 0.073 0.515 0.492 3.975 NaN NaN
elr winter 2017 0.973 0.000e+00 0.491 0.494 3.789 0.610 4.297
baseline winter 2018 0.972 0.171 0.418 0.451 2.001 NaN NaN
elr winter 2018 0.957 0.073 0.417 0.491 2.291 0.635 4.086
baseline winter 2019 0.992 0.150 0.389 0.442 2.857 NaN NaN
elr winter 2019 0.992 0.100 0.302 0.390 2.428 0.585 3.258
baseline all 0.987 0.096 0.423 0.454 3.975 NaN NaN
elr all 0.978 0.037 0.381 0.456 3.789 0.618 3.900

Extended logistic regression plots

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