GMS location: 814

Random forest results

names period power significance meanSquareError absError maxError CRPS IGN
baseline winter 2016 0.984 0.000e+00 0.312 0.426 1.863 NaN NaN
forest winter 2016 0.995 0.000e+00 0.259 0.380 1.841 0.472 4.195
baseline winter 2017 0.991 0.056 0.291 0.406 1.827 NaN NaN
forest winter 2017 1.000 0.056 0.212 0.333 1.439 0.465 4.261
baseline winter 2018 0.979 0.053 0.357 0.424 2.447 NaN NaN
forest winter 2018 0.972 0.000e+00 0.293 0.386 2.356 0.477 4.258
baseline winter 2019 1.000 0.077 0.297 0.389 1.874 NaN NaN
forest winter 2019 1.000 0.077 0.275 0.378 2.238 0.437 2.857
baseline all 0.988 0.048 0.315 0.413 2.447 NaN NaN
forest all 0.991 0.036 0.260 0.370 2.356 0.464 3.944

Random forest plots

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Extended logistic regression results

names period power significance meanSquareError absError maxError CRPS IGN
baseline winter 2016 0.984 0.000e+00 0.312 0.426 1.863 NaN NaN
elr winter 2016 0.979 0.067 0.285 0.410 1.716 0.570 6.659
baseline winter 2017 0.991 0.056 0.291 0.406 1.827 NaN NaN
elr winter 2017 0.983 0.028 0.214 0.371 1.452 0.562 4.881
baseline winter 2018 0.979 0.053 0.357 0.424 2.447 NaN NaN
elr winter 2018 0.972 0.000e+00 0.284 0.399 2.070 0.519 5.562
baseline winter 2019 1.000 0.077 0.297 0.389 1.874 NaN NaN
elr winter 2019 1.000 0.000e+00 0.260 0.367 2.011 0.508 4.548
baseline all 0.988 0.048 0.315 0.413 2.447 NaN NaN
elr all 0.983 0.024 0.263 0.389 2.070 0.542 5.530

Extended logistic regression plots

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