GMS location: 833

Random forest results

names period power significance meanSquareError absError maxError CRPS IGN
baseline winter 2016 0.966 0.037 0.333 0.398 2.699 NaN NaN
forest winter 2016 0.961 0.000e+00 0.331 0.401 2.649 0.546 4.336
baseline winter 2017 0.972 0.067 0.408 0.425 2.577 NaN NaN
forest winter 2017 0.972 0.044 0.336 0.393 2.529 0.503 4.107
baseline winter 2018 0.980 0.065 0.317 0.401 2.199 NaN NaN
forest winter 2018 0.980 0.065 0.299 0.391 2.110 0.515 3.478
baseline winter 2019 1.000 0.045 0.319 0.394 2.164 NaN NaN
forest winter 2019 1.000 0.000e+00 0.251 0.344 2.099 0.521 3.694
baseline all 0.979 0.056 0.342 0.404 2.699 NaN NaN
forest all 0.977 0.032 0.305 0.384 2.649 0.523 3.919

Random forest plots

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Extended logistic regression results

names period power significance meanSquareError absError maxError CRPS IGN
baseline winter 2016 0.966 0.037 0.333 0.398 2.699 NaN NaN
elr winter 2016 0.961 0.000e+00 0.363 0.440 2.382 0.650 6.342
baseline winter 2017 0.972 0.067 0.408 0.425 2.577 NaN NaN
elr winter 2017 0.982 0.044 0.355 0.398 2.483 0.531 4.234
baseline winter 2018 0.980 0.065 0.317 0.401 2.199 NaN NaN
elr winter 2018 0.973 0.129 0.301 0.386 2.367 0.587 4.378
baseline winter 2019 1.000 0.045 0.319 0.394 2.164 NaN NaN
elr winter 2019 1.000 0.000e+00 0.246 0.352 1.799 0.561 3.778
baseline all 0.979 0.056 0.342 0.404 2.699 NaN NaN
elr all 0.977 0.048 0.319 0.397 2.483 0.588 4.788

Extended logistic regression plots

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