GMS location: 571

Random forest results

names period power significance meanSquareError absError maxError CRPS IGN
baseline winter 2016 0.984 0.200 0.319 0.425 1.998 NaN NaN
forest winter 2016 0.984 0.200 0.287 0.412 1.895 0.514 4.722
baseline winter 2017 0.974 0.029 0.400 0.473 2.069 NaN NaN
forest winter 2017 0.966 0.057 0.329 0.428 1.834 0.492 4.398
baseline winter 2018 0.993 0.029 0.341 0.437 2.002 NaN NaN
forest winter 2018 0.993 0.088 0.284 0.396 1.991 0.507 3.130
baseline winter 2019 0.991 0.000e+00 0.268 0.392 1.527 NaN NaN
forest winter 2019 0.983 0.000e+00 0.242 0.372 1.512 0.497 3.133
baseline all 0.986 0.037 0.336 0.434 2.069 NaN NaN
forest all 0.982 0.074 0.288 0.403 1.991 0.503 3.809

Random forest plots

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Extended logistic regression results

names period power significance meanSquareError absError maxError CRPS IGN
baseline winter 2016 0.984 0.200 0.319 0.425 1.998 NaN NaN
elr winter 2016 0.984 0.200 0.316 0.449 1.670 0.624 5.592
baseline winter 2017 0.974 0.029 0.400 0.473 2.069 NaN NaN
elr winter 2017 0.957 0.057 0.397 0.472 2.367 0.566 5.670
baseline winter 2018 0.993 0.029 0.341 0.437 2.002 NaN NaN
elr winter 2018 0.993 0.029 0.300 0.393 2.119 0.571 6.092
baseline winter 2019 0.991 0.000e+00 0.268 0.392 1.527 NaN NaN
elr winter 2019 0.991 0.000e+00 0.327 0.437 1.485 0.562 4.810
baseline all 0.986 0.037 0.336 0.434 2.069 NaN NaN
elr all 0.982 0.049 0.334 0.435 2.367 0.580 5.606

Extended logistic regression plots

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