GMS location: 427

Random forest results

names period power significance meanSquareError absError maxError CRPS IGN
baseline winter 2016 0.982 0.172 0.298 0.401 2.011 NaN NaN
forest winter 2016 0.963 0.138 0.274 0.402 1.946 0.503 2.741
baseline winter 2017 0.962 0.067 0.515 0.528 2.344 NaN NaN
forest winter 2017 0.962 0.067 0.394 0.465 2.052 0.489 2.995
baseline winter 2018 0.993 0.077 0.405 0.445 3.006 NaN NaN
forest winter 2018 0.985 0.077 0.343 0.417 2.640 0.490 2.773
baseline winter 2019 0.992 0.056 0.415 0.469 2.446 NaN NaN
forest winter 2019 0.992 0.000e+00 0.325 0.411 1.928 0.489 2.497
baseline all 0.983 0.092 0.401 0.457 3.006 NaN NaN
forest all 0.976 0.076 0.331 0.422 2.640 0.493 2.752

Random forest plots

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Extended logistic regression results

names period power significance meanSquareError absError maxError CRPS IGN
baseline winter 2016 0.982 0.172 0.298 0.401 2.011 NaN NaN
elr winter 2016 0.957 0.035 0.295 0.426 1.780 0.596 3.618
baseline winter 2017 0.962 0.067 0.515 0.528 2.344 NaN NaN
elr winter 2017 0.962 0.067 0.437 0.505 2.152 0.542 3.652
baseline winter 2018 0.993 0.077 0.405 0.445 3.006 NaN NaN
elr winter 2018 0.993 0.077 0.347 0.429 2.457 0.552 3.460
baseline winter 2019 0.992 0.056 0.415 0.469 2.446 NaN NaN
elr winter 2019 0.992 0.056 0.321 0.423 1.968 0.536 2.999
baseline all 0.983 0.092 0.401 0.457 3.006 NaN NaN
elr all 0.976 0.061 0.346 0.444 2.457 0.559 3.446

Extended logistic regression plots

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