GMS location: 415

Random forest results

names period power significance meanSquareError absError maxError CRPS IGN
baseline winter 2016 1.000 0.069 0.289 0.419 2.100 NaN NaN
forest winter 2016 1.000 0.000e+00 0.226 0.351 2.084 0.479 5.553
baseline winter 2017 0.981 0.043 0.398 0.433 2.829 NaN NaN
forest winter 2017 0.981 0.043 0.254 0.361 1.839 0.452 3.872
baseline winter 2018 0.969 0.111 0.346 0.424 2.280 NaN NaN
forest winter 2018 0.985 0.056 0.257 0.368 1.889 0.469 4.073
baseline winter 2019 0.965 0.000e+00 0.369 0.432 2.531 NaN NaN
forest winter 2019 0.986 0.000e+00 0.254 0.356 1.889 0.462 3.540
baseline all 0.979 0.063 0.348 0.427 2.829 NaN NaN
forest all 0.989 0.032 0.247 0.359 2.084 0.466 4.305

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 0.069 0.289 0.419 2.100 NaN NaN
elr winter 2016 0.993 0.000e+00 0.274 0.411 2.031 0.528 5.335
baseline winter 2017 0.981 0.043 0.398 0.433 2.829 NaN NaN
elr winter 2017 0.991 0.021 0.316 0.399 2.333 0.515 5.163
baseline winter 2018 0.969 0.111 0.346 0.424 2.280 NaN NaN
elr winter 2018 0.985 0.056 0.260 0.385 2.157 0.536 5.247
baseline winter 2019 0.965 0.000e+00 0.369 0.432 2.531 NaN NaN
elr winter 2019 0.986 0.000e+00 0.302 0.399 1.840 0.506 4.978
baseline all 0.979 0.063 0.348 0.427 2.829 NaN NaN
elr all 0.989 0.024 0.287 0.399 2.333 0.522 5.187

Extended logistic regression plots

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