GMS location: 426

Random forest results

names period power significance meanSquareError absError maxError CRPS IGN
baseline winter 2016 0.952 0.120 1.127 0.667 6.555 NaN NaN
forest winter 2016 0.939 0.120 1.034 0.628 6.335 0.503 6.391
baseline winter 2017 0.982 0.024 0.360 0.431 2.205 NaN NaN
forest winter 2017 0.991 0.000e+00 0.306 0.415 1.700 0.480 1.497
baseline winter 2018 1.000 0.107 0.313 0.399 2.314 NaN NaN
forest winter 2018 1.000 0.107 0.280 0.400 2.004 0.465 1.381
baseline winter 2019 1.000 0.000e+00 0.243 0.364 1.812 NaN NaN
forest winter 2019 1.000 0.000e+00 0.209 0.342 1.561 0.461 1.339
baseline all 0.981 0.068 0.557 0.481 6.555 NaN NaN
forest all 0.979 0.058 0.501 0.461 6.335 0.479 2.926

Random forest plots

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Extended logistic regression results

names period power significance meanSquareError absError maxError CRPS IGN
baseline winter 2016 0.952 0.120 1.127 0.667 6.555 NaN NaN
elr winter 2016 0.927 0.120 1.071 0.666 6.287 0.566 5.027
baseline winter 2017 0.982 0.024 0.360 0.431 2.205 NaN NaN
elr winter 2017 0.991 0.024 0.345 0.438 2.075 0.467 1.495
baseline winter 2018 1.000 0.107 0.313 0.399 2.314 NaN NaN
elr winter 2018 0.992 0.143 0.295 0.396 1.941 0.490 1.606
baseline winter 2019 1.000 0.000e+00 0.243 0.364 1.812 NaN NaN
elr winter 2019 1.000 0.111 0.247 0.370 1.498 0.464 1.420
baseline all 0.981 0.068 0.557 0.481 6.555 NaN NaN
elr all 0.973 0.087 0.533 0.483 6.287 0.502 2.581

Extended logistic regression plots

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