GMS location: 1154

Random forest results

names period power significance meanSquareError absError maxError CRPS IGN
baseline winter 2016 1.000 0.030 0.422 0.473 2.380 NaN NaN
forest winter 2016 1.000 0.030 0.328 0.425 2.013 0.568 2.052
baseline winter 2017 0.983 0.000e+00 0.911 0.631 4.066 NaN NaN
forest winter 2017 0.992 0.000e+00 0.749 0.576 3.663 0.586 3.928
baseline winter 2018 0.971 0.100 0.403 0.447 3.085 NaN NaN
forest winter 2018 0.978 0.050 0.375 0.445 2.963 0.598 2.171
baseline winter 2019 0.993 0.077 0.434 0.495 2.105 NaN NaN
forest winter 2019 0.993 0.077 0.287 0.407 1.936 0.603 2.472
baseline all 0.988 0.049 0.529 0.506 4.066 NaN NaN
forest all 0.991 0.033 0.425 0.460 3.663 0.588 2.598

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 0.030 0.422 0.473 2.380 NaN NaN
elr winter 2016 1.000 0.030 0.352 0.467 1.937 0.657 2.889
baseline winter 2017 0.983 0.000e+00 0.911 0.631 4.066 NaN NaN
elr winter 2017 0.983 0.000e+00 0.766 0.608 3.702 0.660 4.724
baseline winter 2018 0.971 0.100 0.403 0.447 3.085 NaN NaN
elr winter 2018 0.964 0.050 0.407 0.500 2.972 0.673 2.962
baseline winter 2019 0.993 0.077 0.434 0.495 2.105 NaN NaN
elr winter 2019 0.993 0.077 0.340 0.468 1.965 0.682 2.954
baseline all 0.988 0.049 0.529 0.506 4.066 NaN NaN
elr all 0.986 0.033 0.456 0.507 3.702 0.668 3.333

Extended logistic regression plots

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