GMS location: 722

Random forest results

names period power significance meanSquareError absError maxError CRPS IGN
baseline winter 2016 1.000 0.067 0.315 0.410 2.127 NaN NaN
forest winter 2016 1.000 0.133 0.289 0.394 1.992 0.502 2.961
baseline winter 2017 0.984 0.067 0.421 0.458 2.562 NaN NaN
forest winter 2017 0.975 0.067 0.354 0.430 2.348 0.501 3.213
baseline winter 2018 0.994 0.000e+00 0.345 0.426 2.264 NaN NaN
forest winter 2018 0.994 0.000e+00 0.333 0.410 2.495 0.505 2.883
baseline winter 2019 0.987 0.000e+00 0.284 0.375 2.214 NaN NaN
forest winter 2019 0.987 0.000e+00 0.260 0.380 1.627 0.493 2.564
baseline all 0.992 0.038 0.339 0.417 2.562 NaN NaN
forest all 0.990 0.051 0.308 0.403 2.495 0.500 2.906

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 0.067 0.315 0.410 2.127 NaN NaN
elr winter 2016 0.995 0.067 0.309 0.411 2.031 0.557 3.863
baseline winter 2017 0.984 0.067 0.421 0.458 2.562 NaN NaN
elr winter 2017 0.975 0.067 0.406 0.464 2.481 0.584 4.791
baseline winter 2018 0.994 0.000e+00 0.345 0.426 2.264 NaN NaN
elr winter 2018 0.987 0.000e+00 0.367 0.449 2.445 0.557 4.163
baseline winter 2019 0.987 0.000e+00 0.284 0.375 2.214 NaN NaN
elr winter 2019 0.987 0.000e+00 0.260 0.391 1.642 0.519 3.256
baseline all 0.992 0.038 0.339 0.417 2.562 NaN NaN
elr all 0.987 0.038 0.334 0.428 2.481 0.554 4.008

Extended logistic regression plots

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