GMS location: 1433

Random forest results

names period power significance meanSquareError absError maxError CRPS IGN
baseline winter 2016 1.000 0.097 0.282 0.391 2.249 NaN NaN
forest winter 2016 0.994 0.097 0.258 0.373 2.150 0.498 4.408
baseline winter 2017 0.971 0.024 0.380 0.431 2.622 NaN NaN
forest winter 2017 0.981 0.000e+00 0.331 0.413 2.106 0.483 5.332
baseline winter 2018 0.985 0.057 0.340 0.419 2.023 NaN NaN
forest winter 2018 0.977 0.000e+00 0.299 0.380 2.147 0.495 4.754
baseline winter 2019 0.985 0.000e+00 0.335 0.419 2.082 NaN NaN
forest winter 2019 0.992 0.000e+00 0.264 0.382 1.922 0.476 3.587
baseline all 0.987 0.049 0.330 0.413 2.622 NaN NaN
forest all 0.987 0.025 0.286 0.385 2.150 0.489 4.523

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 0.097 0.282 0.391 2.249 NaN NaN
elr winter 2016 0.983 0.097 0.293 0.406 2.463 0.565 5.230
baseline winter 2017 0.971 0.024 0.380 0.431 2.622 NaN NaN
elr winter 2017 0.971 0.024 0.316 0.409 2.156 0.563 5.051
baseline winter 2018 0.985 0.057 0.340 0.419 2.023 NaN NaN
elr winter 2018 0.977 0.000e+00 0.313 0.408 2.020 0.554 5.200
baseline winter 2019 0.985 0.000e+00 0.335 0.419 2.082 NaN NaN
elr winter 2019 0.992 0.000e+00 0.304 0.429 1.929 0.519 4.314
baseline all 0.987 0.049 0.330 0.413 2.622 NaN NaN
elr all 0.982 0.033 0.305 0.412 2.463 0.552 4.985

Extended logistic regression plots

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