GMS location: 430

Random forest results

names period power significance meanSquareError absError maxError CRPS IGN
baseline winter 2016 0.958 0.116 0.708 0.623 2.987 NaN NaN
forest winter 2016 0.958 0.070 0.555 0.550 2.278 0.472 2.140
baseline winter 2017 0.969 0.054 1.160 0.749 3.668 NaN NaN
forest winter 2017 0.990 0.036 0.696 0.619 2.591 0.468 2.008
baseline winter 2018 0.965 0.095 0.504 0.516 2.354 NaN NaN
forest winter 2018 0.965 0.095 0.538 0.555 2.541 0.484 1.855
baseline winter 2019 0.990 0.051 0.785 0.600 3.935 NaN NaN
forest winter 2019 1.000 0.128 0.540 0.518 2.307 0.466 1.663
baseline all 0.969 0.078 0.784 0.622 3.935 NaN NaN
forest all 0.976 0.078 0.581 0.561 2.591 0.473 1.932

Random forest plots

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Extended logistic regression results

names period power significance meanSquareError absError maxError CRPS IGN
baseline winter 2016 0.958 0.116 0.708 0.623 2.987 NaN NaN
elr winter 2016 0.972 0.070 0.591 0.594 2.354 0.516 1.657
baseline winter 2017 0.969 0.054 1.160 0.749 3.668 NaN NaN
elr winter 2017 0.990 0.054 0.867 0.691 3.340 0.522 2.047
baseline winter 2018 0.965 0.095 0.504 0.516 2.354 NaN NaN
elr winter 2018 0.965 0.143 0.571 0.610 2.790 0.566 1.897
baseline winter 2019 0.990 0.051 0.785 0.600 3.935 NaN NaN
elr winter 2019 1.000 0.179 0.564 0.553 2.896 0.497 1.553
baseline all 0.969 0.078 0.784 0.622 3.935 NaN NaN
elr all 0.980 0.106 0.646 0.612 3.340 0.525 1.786

Extended logistic regression plots

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