GMS location: 530

Random forest results

names period power significance meanSquareError absError maxError CRPS IGN
baseline winter 2016 0.979 0.000e+00 0.371 0.454 1.975 NaN NaN
forest winter 2016 0.989 0.000e+00 0.290 0.404 1.854 0.503 3.279
baseline winter 2017 0.969 0.000e+00 0.459 0.484 2.798 NaN NaN
forest winter 2017 0.977 0.040 0.343 0.412 2.701 0.490 4.184
baseline winter 2018 0.987 0.103 0.375 0.446 2.220 NaN NaN
forest winter 2018 0.994 0.138 0.326 0.420 1.983 0.494 3.045
baseline winter 2019 0.985 0.125 0.262 0.385 1.789 NaN NaN
forest winter 2019 0.992 0.250 0.201 0.351 1.587 0.528 3.476
baseline all 0.980 0.051 0.369 0.444 2.798 NaN NaN
forest all 0.988 0.090 0.293 0.399 2.701 0.503 3.461

Random forest plots

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Extended logistic regression results

names period power significance meanSquareError absError maxError CRPS IGN
baseline winter 2016 0.979 0.000e+00 0.371 0.454 1.975 NaN NaN
elr winter 2016 0.979 0.000e+00 0.347 0.454 2.186 0.560 5.392
baseline winter 2017 0.969 0.000e+00 0.459 0.484 2.798 NaN NaN
elr winter 2017 0.977 0.000e+00 0.444 0.491 2.525 0.568 5.657
baseline winter 2018 0.987 0.103 0.375 0.446 2.220 NaN NaN
elr winter 2018 0.980 0.103 0.367 0.444 2.387 0.615 6.464
baseline winter 2019 0.985 0.125 0.262 0.385 1.789 NaN NaN
elr winter 2019 0.985 0.250 0.191 0.326 1.481 0.581 4.165
baseline all 0.980 0.051 0.369 0.444 2.798 NaN NaN
elr all 0.980 0.064 0.342 0.433 2.525 0.581 5.487

Extended logistic regression plots

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