GMS location: 818

Random forest results

names period power significance meanSquareError absError maxError CRPS IGN
baseline winter 2016 0.913 0.000e+00 0.482 0.554 2.260 NaN NaN
forest winter 2016 1.000 0.000e+00 0.357 0.485 1.701 0.363 1.182
baseline winter 2017 0.983 0.083 0.568 0.538 2.317 NaN NaN
forest winter 2017 0.991 0.083 0.326 0.415 2.125 0.411 2.022
baseline winter 2018 0.987 0.042 0.594 0.567 2.898 NaN NaN
forest winter 2018 0.980 0.083 0.344 0.430 2.525 0.411 2.193
baseline winter 2019 1.000 NaN 0.696 0.719 1.495 NaN NaN
forest winter 2019 1.000 NaN 0.403 0.463 1.777 0.376 1.325
baseline all 0.976 0.066 0.578 0.564 2.898 NaN NaN
forest all 0.988 0.082 0.343 0.433 2.525 0.403 1.953

Random forest plots

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Extended logistic regression results

names period power significance meanSquareError absError maxError CRPS IGN
baseline winter 2016 0.913 0.000e+00 0.482 0.554 2.260 NaN NaN
elr winter 2016 0.935 0.000e+00 0.426 0.495 2.023 0.548 4.089
baseline winter 2017 0.983 0.083 0.568 0.538 2.317 NaN NaN
elr winter 2017 0.983 0.083 0.306 0.415 1.875 0.543 3.732
baseline winter 2018 0.987 0.042 0.594 0.567 2.898 NaN NaN
elr winter 2018 0.987 0.125 0.365 0.459 2.026 0.554 4.650
baseline winter 2019 1.000 NaN 0.696 0.719 1.495 NaN NaN
elr winter 2019 1.000 NaN 0.365 0.404 1.977 0.519 3.520
baseline all 0.976 0.066 0.578 0.564 2.898 NaN NaN
elr all 0.979 0.098 0.350 0.443 2.026 0.547 4.163

Extended logistic regression plots

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