GMS location: 601

Random forest results

names period power significance meanSquareError absError maxError CRPS IGN
baseline winter 2016 1.000 0.000e+00 0.359 0.427 2.632 NaN NaN
forest winter 2016 0.995 0.000e+00 0.310 0.417 2.935 0.557 3.134
baseline winter 2017 0.991 0.057 0.528 0.514 2.894 NaN NaN
forest winter 2017 0.982 0.057 0.448 0.467 2.834 0.533 3.960
baseline winter 2018 0.985 0.176 0.422 0.476 2.540 NaN NaN
forest winter 2018 0.985 0.176 0.338 0.431 2.248 0.546 3.224
baseline winter 2019 0.994 0.000e+00 0.315 0.414 2.124 NaN NaN
forest winter 2019 1.000 0.000e+00 0.236 0.344 1.812 0.529 2.377
baseline all 0.993 0.082 0.400 0.455 2.894 NaN NaN
forest all 0.991 0.082 0.329 0.413 2.935 0.542 3.150

Random forest plots

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Extended logistic regression results

names period power significance meanSquareError absError maxError CRPS IGN
baseline winter 2016 1.000 0.000e+00 0.359 0.427 2.632 NaN NaN
elr winter 2016 0.984 0.000e+00 0.357 0.448 3.139 0.617 4.807
baseline winter 2017 0.991 0.057 0.528 0.514 2.894 NaN NaN
elr winter 2017 0.973 0.057 0.491 0.499 2.984 0.585 5.139
baseline winter 2018 0.985 0.176 0.422 0.476 2.540 NaN NaN
elr winter 2018 0.985 0.176 0.385 0.475 2.546 0.606 4.403
baseline winter 2019 0.994 0.000e+00 0.315 0.414 2.124 NaN NaN
elr winter 2019 0.994 0.000e+00 0.254 0.358 2.040 0.579 3.468
baseline all 0.993 0.082 0.400 0.455 2.894 NaN NaN
elr all 0.985 0.082 0.368 0.443 3.139 0.598 4.454

Extended logistic regression plots

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