GMS location: 564

Random forest results

names period power significance meanSquareError absError maxError CRPS IGN
baseline winter 2016 0.977 0.103 1.715 0.858 4.598 NaN NaN
forest winter 2016 0.983 0.138 1.669 0.848 4.296 0.479 2.914
baseline winter 2017 0.954 0.000e+00 0.378 0.446 2.142 NaN NaN
forest winter 2017 0.962 0.000e+00 0.353 0.429 1.968 0.477 1.343
baseline winter 2018 0.981 0.045 0.418 0.467 3.069 NaN NaN
forest winter 2018 0.987 0.091 0.362 0.424 3.041 0.472 1.366
baseline winter 2019 0.993 0.100 0.259 0.367 2.099 NaN NaN
forest winter 2019 1.000 0.000e+00 0.260 0.377 1.995 0.489 1.390
baseline all 0.977 0.059 0.762 0.557 4.598 NaN NaN
forest all 0.984 0.071 0.728 0.542 4.296 0.479 1.829

Random forest plots

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Extended logistic regression results

names period power significance meanSquareError absError maxError CRPS IGN
baseline winter 2016 0.977 0.103 1.715 0.858 4.598 NaN NaN
elr winter 2016 0.983 0.103 1.753 0.863 4.206 0.554 4.291
baseline winter 2017 0.954 0.000e+00 0.378 0.446 2.142 NaN NaN
elr winter 2017 0.969 0.000e+00 0.354 0.470 1.970 0.508 1.441
baseline winter 2018 0.981 0.045 0.418 0.467 3.069 NaN NaN
elr winter 2018 0.987 0.045 0.485 0.521 3.146 0.541 1.638
baseline winter 2019 0.993 0.100 0.259 0.367 2.099 NaN NaN
elr winter 2019 0.993 0.100 0.311 0.427 2.188 0.504 1.425
baseline all 0.977 0.059 0.762 0.557 4.598 NaN NaN
elr all 0.984 0.059 0.797 0.591 4.206 0.529 2.341

Extended logistic regression plots

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