GMS location: 515

Random forest results

names period power significance meanSquareError absError maxError CRPS IGN
baseline winter 2016 0.994 0.167 0.369 0.452 2.198 NaN NaN
forest winter 2016 0.977 0.125 0.328 0.416 2.301 0.522 1.634
baseline winter 2017 0.991 0.068 0.562 0.542 3.501 NaN NaN
forest winter 2017 0.991 0.045 0.473 0.484 3.339 0.503 1.879
baseline winter 2018 0.992 0.167 1.508 0.736 6.500 NaN NaN
forest winter 2018 0.977 0.167 1.365 0.701 5.705 0.553 3.050
baseline winter 2019 0.993 0.000e+00 0.296 0.407 1.798 NaN NaN
forest winter 2019 0.985 0.000e+00 0.235 0.363 1.448 0.499 1.670
baseline all 0.993 0.109 0.677 0.532 6.500 NaN NaN
forest all 0.982 0.091 0.596 0.490 5.705 0.520 2.047

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 0.167 0.369 0.452 2.198 NaN NaN
elr winter 2016 0.988 0.167 0.399 0.468 2.384 0.582 1.768
baseline winter 2017 0.991 0.068 0.562 0.542 3.501 NaN NaN
elr winter 2017 0.972 0.045 0.524 0.528 3.600 0.545 1.696
baseline winter 2018 0.992 0.167 1.508 0.736 6.500 NaN NaN
elr winter 2018 0.977 0.167 1.431 0.732 5.906 0.636 5.062
baseline winter 2019 0.993 0.000e+00 0.296 0.407 1.798 NaN NaN
elr winter 2019 0.985 0.000e+00 0.221 0.372 1.456 0.542 1.557
baseline all 0.993 0.109 0.677 0.532 6.500 NaN NaN
elr all 0.982 0.100 0.641 0.525 5.906 0.578 2.515

Extended logistic regression plots

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