GMS location: 603

Random forest results

names period power significance meanSquareError absError maxError CRPS IGN
baseline winter 2016 0.995 0.000e+00 0.311 0.425 2.102 NaN NaN
forest winter 2016 0.995 0.000e+00 0.254 0.378 2.051 0.497 2.694
baseline winter 2017 0.964 0.081 0.474 0.497 2.771 NaN NaN
forest winter 2017 0.955 0.108 0.374 0.442 2.040 0.480 3.281
baseline winter 2018 0.993 0.100 0.396 0.456 2.243 NaN NaN
forest winter 2018 0.993 0.133 0.322 0.422 2.263 0.504 2.813
baseline winter 2019 0.993 0.000e+00 0.364 0.444 2.491 NaN NaN
forest winter 2019 1.000 0.000e+00 0.311 0.395 2.803 0.486 3.141
baseline all 0.988 0.060 0.380 0.453 2.771 NaN NaN
forest all 0.988 0.080 0.310 0.407 2.803 0.493 2.957

Random forest plots

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Extended logistic regression results

names period power significance meanSquareError absError maxError CRPS IGN
baseline winter 2016 0.995 0.000e+00 0.311 0.425 2.102 NaN NaN
elr winter 2016 0.989 0.000e+00 0.295 0.424 1.960 0.563 4.005
baseline winter 2017 0.964 0.081 0.474 0.497 2.771 NaN NaN
elr winter 2017 0.964 0.108 0.419 0.460 2.421 0.489 3.231
baseline winter 2018 0.993 0.100 0.396 0.456 2.243 NaN NaN
elr winter 2018 0.993 0.100 0.352 0.448 2.282 0.555 4.255
baseline winter 2019 0.993 0.000e+00 0.364 0.444 2.491 NaN NaN
elr winter 2019 0.993 0.000e+00 0.351 0.460 2.290 0.497 3.014
baseline all 0.988 0.060 0.380 0.453 2.771 NaN NaN
elr all 0.986 0.070 0.350 0.446 2.421 0.529 3.666

Extended logistic regression plots

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