GMS location: 1173

Random forest results

names period power significance meanSquareError absError maxError CRPS IGN
baseline winter 2016 0.994 0.094 0.311 0.411 2.161 NaN NaN
forest winter 2016 0.994 0.062 0.286 0.395 1.903 0.550 3.413
baseline winter 2017 0.975 0.000e+00 0.392 0.456 1.920 NaN NaN
forest winter 2017 0.992 0.000e+00 0.308 0.416 1.661 0.532 4.010
baseline winter 2018 0.985 0.088 0.487 0.468 2.514 NaN NaN
forest winter 2018 0.978 0.059 0.436 0.456 2.491 0.542 4.054
baseline winter 2019 0.992 0.000e+00 0.379 0.437 1.851 NaN NaN
forest winter 2019 1.000 0.000e+00 0.241 0.370 1.504 0.562 4.024
baseline all 0.988 0.054 0.388 0.441 2.514 NaN NaN
forest all 0.991 0.036 0.320 0.410 2.491 0.547 3.840

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 0.094 0.311 0.411 2.161 NaN NaN
elr winter 2016 0.994 0.094 0.303 0.426 1.895 0.630 4.005
baseline winter 2017 0.975 0.000e+00 0.392 0.456 1.920 NaN NaN
elr winter 2017 0.983 0.000e+00 0.368 0.451 1.913 0.581 3.924
baseline winter 2018 0.985 0.088 0.487 0.468 2.514 NaN NaN
elr winter 2018 0.978 0.147 0.450 0.489 2.561 0.624 5.105
baseline winter 2019 0.992 0.000e+00 0.379 0.437 1.851 NaN NaN
elr winter 2019 0.992 0.000e+00 0.350 0.460 1.698 0.613 4.075
baseline all 0.988 0.054 0.388 0.441 2.514 NaN NaN
elr all 0.988 0.073 0.365 0.455 2.561 0.614 4.279

Extended logistic regression plots

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