GMS location: 503

Random forest results

names period power significance meanSquareError absError maxError CRPS IGN
baseline winter 2016 0.994 0.242 0.459 0.500 2.960 NaN NaN
forest winter 2016 0.994 0.242 0.454 0.517 2.661 0.581 2.395
baseline winter 2017 0.974 0.079 0.496 0.499 2.801 NaN NaN
forest winter 2017 0.957 0.079 0.466 0.486 2.877 0.536 1.957
baseline winter 2018 0.991 0.250 0.430 0.490 1.998 NaN NaN
forest winter 2018 0.991 0.062 0.333 0.418 2.342 0.581 2.002
baseline winter 2019 0.985 0.316 0.633 0.574 2.892 NaN NaN
forest winter 2019 0.978 0.158 0.559 0.495 3.274 0.567 2.372
baseline all 0.987 0.205 0.503 0.515 2.960 NaN NaN
forest all 0.981 0.131 0.455 0.483 3.274 0.567 2.201

Random forest plots

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Extended logistic regression results

names period power significance meanSquareError absError maxError CRPS IGN
baseline winter 2016 0.994 0.242 0.459 0.500 2.960 NaN NaN
elr winter 2016 0.994 0.151 0.553 0.589 2.543 0.727 4.781
baseline winter 2017 0.974 0.079 0.496 0.499 2.801 NaN NaN
elr winter 2017 0.965 0.079 0.481 0.497 3.147 0.580 2.982
baseline winter 2018 0.991 0.250 0.430 0.490 1.998 NaN NaN
elr winter 2018 0.991 0.125 0.366 0.449 2.061 0.616 2.837
baseline winter 2019 0.985 0.316 0.633 0.574 2.892 NaN NaN
elr winter 2019 0.978 0.053 0.574 0.513 3.411 0.656 3.560
baseline all 0.987 0.205 0.503 0.515 2.960 NaN NaN
elr all 0.983 0.107 0.500 0.519 3.411 0.651 3.644

Extended logistic regression plots

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