GMS location: 1403

Random forest results

names period power significance meanSquareError absError maxError CRPS IGN
baseline winter 2016 0.994 0.100 0.328 0.426 1.938 NaN NaN
forest winter 2016 0.983 0.067 0.282 0.394 1.754 0.476 3.055
baseline winter 2017 0.991 0.021 0.522 0.518 2.550 NaN NaN
forest winter 2017 0.981 0.000e+00 0.519 0.521 2.407 0.460 4.124
baseline winter 2018 0.977 0.139 0.402 0.449 2.445 NaN NaN
forest winter 2018 0.977 0.056 0.366 0.422 2.351 0.486 4.049
baseline winter 2019 0.982 0.045 0.365 0.406 2.441 NaN NaN
forest winter 2019 1.000 0.091 0.312 0.388 2.444 0.451 2.376
baseline all 0.987 0.074 0.399 0.449 2.550 NaN NaN
forest all 0.985 0.044 0.365 0.429 2.444 0.470 3.414

Random forest plots

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Extended logistic regression results

names period power significance meanSquareError absError maxError CRPS IGN
baseline winter 2016 0.994 0.100 0.328 0.426 1.938 NaN NaN
elr winter 2016 0.983 0.033 0.327 0.436 1.973 0.538 2.994
baseline winter 2017 0.991 0.021 0.522 0.518 2.550 NaN NaN
elr winter 2017 0.991 0.000e+00 0.473 0.494 2.120 0.520 3.643
baseline winter 2018 0.977 0.139 0.402 0.449 2.445 NaN NaN
elr winter 2018 0.977 0.056 0.396 0.458 2.275 0.547 3.714
baseline winter 2019 0.982 0.045 0.365 0.406 2.441 NaN NaN
elr winter 2019 1.000 0.091 0.301 0.396 2.188 0.511 2.889
baseline all 0.987 0.074 0.399 0.449 2.550 NaN NaN
elr all 0.987 0.037 0.373 0.447 2.275 0.530 3.304

Extended logistic regression plots

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