GMS location: 209

Random forest results

names period power significance meanSquareError absError maxError CRPS IGN
baseline winter 2016 0.984 0.000e+00 0.331 0.440 2.072 NaN NaN
forest winter 2016 1.000 0.000e+00 0.243 0.364 1.877 0.452 4.978
baseline winter 2017 0.968 0.000e+00 0.395 0.481 2.128 NaN NaN
forest winter 2017 0.992 0.000e+00 0.260 0.392 1.402 0.462 5.283
baseline winter 2018 0.981 0.043 0.324 0.431 1.590 NaN NaN
forest winter 2018 0.994 0.043 0.224 0.360 1.452 0.465 3.639
baseline winter 2019 0.992 0.077 0.328 0.434 2.175 NaN NaN
forest winter 2019 0.992 0.077 0.222 0.347 1.437 0.453 4.321
baseline all 0.981 0.025 0.343 0.446 2.175 NaN NaN
forest all 0.995 0.025 0.238 0.366 1.877 0.458 4.563

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 0.000e+00 0.331 0.440 2.072 NaN NaN
elr winter 2016 1.000 0.000e+00 0.257 0.379 1.890 0.542 7.326
baseline winter 2017 0.968 0.000e+00 0.395 0.481 2.128 NaN NaN
elr winter 2017 1.000 0.036 0.241 0.374 1.542 0.506 6.083
baseline winter 2018 0.981 0.043 0.324 0.431 1.590 NaN NaN
elr winter 2018 0.981 0.043 0.265 0.404 1.564 0.526 5.645
baseline winter 2019 0.992 0.077 0.328 0.434 2.175 NaN NaN
elr winter 2019 1.000 0.077 0.205 0.335 1.671 0.519 5.984
baseline all 0.981 0.025 0.343 0.446 2.175 NaN NaN
elr all 0.995 0.038 0.245 0.376 1.890 0.525 6.329

Extended logistic regression plots

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