GMS location: 533

Random forest results

names period power significance meanSquareError absError maxError CRPS IGN
baseline winter 2016 0.995 0.133 0.328 0.432 1.794 NaN NaN
forest winter 2016 0.990 0.133 0.224 0.352 1.849 0.623 6.519
baseline winter 2017 0.992 0.179 0.390 0.475 2.096 NaN NaN
forest winter 2017 0.984 0.143 0.321 0.417 2.252 0.599 7.788
baseline winter 2018 0.987 0.045 0.353 0.404 3.465 NaN NaN
forest winter 2018 0.981 0.045 0.328 0.370 3.608 0.645 7.473
baseline winter 2019 1.000 0.125 0.215 0.353 1.390 NaN NaN
forest winter 2019 1.000 0.062 0.147 0.286 1.262 0.639 5.017
baseline all 0.994 0.123 0.322 0.416 3.465 NaN NaN
forest all 0.989 0.099 0.254 0.356 3.608 0.627 6.694

Random forest plots

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Extended logistic regression results

names period power significance meanSquareError absError maxError CRPS IGN
baseline winter 2016 0.995 0.133 0.328 0.432 1.794 NaN NaN
elr winter 2016 0.990 0.133 0.250 0.388 1.708 0.736 1.012e+01
baseline winter 2017 0.992 0.179 0.390 0.475 2.096 NaN NaN
elr winter 2017 0.976 0.107 0.392 0.466 2.510 0.684 1.140e+01
baseline winter 2018 0.987 0.045 0.353 0.404 3.465 NaN NaN
elr winter 2018 0.981 0.045 0.334 0.399 3.519 0.753 1.340e+01
baseline winter 2019 1.000 0.125 0.215 0.353 1.390 NaN NaN
elr winter 2019 1.000 0.062 0.207 0.339 1.738 0.676 8.102
baseline all 0.994 0.123 0.322 0.416 3.465 NaN NaN
elr all 0.987 0.086 0.293 0.397 3.519 0.715 1.077e+01

Extended logistic regression plots

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