GMS location: 552

Random forest results

names period power significance meanSquareError absError maxError CRPS IGN
baseline winter 2016 0.994 0.000e+00 0.335 0.423 2.599 NaN NaN
forest winter 2016 1.000 0.056 0.262 0.376 2.518 0.447 3.912
baseline winter 2017 0.967 0.000e+00 0.382 0.456 2.251 NaN NaN
forest winter 2017 0.959 0.031 0.262 0.387 1.847 0.460 4.019
baseline winter 2018 0.986 0.095 0.306 0.417 2.077 NaN NaN
forest winter 2018 0.993 0.095 0.251 0.377 1.850 0.462 3.104
baseline winter 2019 0.985 0.111 0.305 0.420 1.662 NaN NaN
forest winter 2019 0.993 0.111 0.301 0.416 1.656 0.449 3.265
baseline all 0.985 0.037 0.332 0.429 2.599 NaN NaN
forest all 0.988 0.062 0.268 0.387 2.518 0.454 3.593

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 0.000e+00 0.335 0.423 2.599 NaN NaN
elr winter 2016 1.000 0.056 0.282 0.412 2.326 0.519 4.602
baseline winter 2017 0.967 0.000e+00 0.382 0.456 2.251 NaN NaN
elr winter 2017 0.967 0.000e+00 0.297 0.418 1.879 0.522 3.979
baseline winter 2018 0.986 0.095 0.306 0.417 2.077 NaN NaN
elr winter 2018 0.993 0.048 0.255 0.374 2.046 0.514 3.646
baseline winter 2019 0.985 0.111 0.305 0.420 1.662 NaN NaN
elr winter 2019 0.993 0.111 0.355 0.458 1.841 0.510 4.106
baseline all 0.985 0.037 0.332 0.429 2.599 NaN NaN
elr all 0.990 0.037 0.294 0.414 2.326 0.516 4.109

Extended logistic regression plots

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