GMS location: 524

Random forest results

names period power significance meanSquareError absError maxError CRPS IGN
baseline winter 2016 0.994 0.000e+00 0.353 0.475 2.100 NaN NaN
forest winter 2016 0.994 0.050 0.263 0.392 1.919 0.484 4.306
baseline winter 2017 0.950 0.062 0.339 0.455 1.577 NaN NaN
forest winter 2017 0.992 0.094 0.232 0.369 1.466 0.469 3.504
baseline winter 2018 0.980 0.138 0.393 0.469 1.967 NaN NaN
forest winter 2018 0.987 0.172 0.293 0.402 1.872 0.479 3.429
baseline winter 2019 0.993 0.000e+00 0.256 0.379 1.625 NaN NaN
forest winter 2019 0.993 0.000e+00 0.217 0.353 1.948 0.474 3.703
baseline all 0.981 0.065 0.339 0.448 2.100 NaN NaN
forest all 0.991 0.098 0.254 0.381 1.948 0.477 3.756

Random forest plots

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Extended logistic regression results

names period power significance meanSquareError absError maxError CRPS IGN
baseline winter 2016 0.994 0.000e+00 0.353 0.475 2.100 NaN NaN
elr winter 2016 0.994 0.100 0.324 0.440 1.870 0.534 4.434
baseline winter 2017 0.950 0.062 0.339 0.455 1.577 NaN NaN
elr winter 2017 0.967 0.094 0.289 0.417 1.493 0.494 3.720
baseline winter 2018 0.980 0.138 0.393 0.469 1.967 NaN NaN
elr winter 2018 0.987 0.138 0.337 0.448 1.657 0.533 4.559
baseline winter 2019 0.993 0.000e+00 0.256 0.379 1.625 NaN NaN
elr winter 2019 0.993 0.000e+00 0.283 0.410 1.848 0.515 3.698
baseline all 0.981 0.065 0.339 0.448 2.100 NaN NaN
elr all 0.986 0.098 0.310 0.430 1.870 0.520 4.143

Extended logistic regression plots

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