GMS location: 101

Random forest results

names period power significance meanSquareError absError maxError CRPS IGN
baseline winter 2016 0.977 0.036 0.390 0.449 2.465 NaN NaN
forest winter 2016 0.966 0.000e+00 0.265 0.372 2.453 0.459 3.677
baseline winter 2017 0.974 0.026 0.560 0.537 2.964 NaN NaN
forest winter 2017 0.983 0.026 0.349 0.417 2.236 0.453 3.703
baseline winter 2018 0.977 0.067 0.441 0.496 2.166 NaN NaN
forest winter 2018 0.985 0.067 0.332 0.416 2.216 0.464 3.826
baseline winter 2019 0.993 0.071 0.310 0.414 1.857 NaN NaN
forest winter 2019 1.000 0.071 0.219 0.350 1.610 0.460 3.428
baseline all 0.981 0.045 0.421 0.471 2.964 NaN NaN
forest all 0.982 0.036 0.289 0.387 2.453 0.459 3.658

Random forest plots

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Extended logistic regression results

names period power significance meanSquareError absError maxError CRPS IGN
baseline winter 2016 0.977 0.036 0.390 0.449 2.465 NaN NaN
elr winter 2016 0.983 0.036 0.321 0.432 2.532 0.528 3.449
baseline winter 2017 0.974 0.026 0.560 0.537 2.964 NaN NaN
elr winter 2017 0.983 0.051 0.380 0.447 2.323 0.455 2.833
baseline winter 2018 0.977 0.067 0.441 0.496 2.166 NaN NaN
elr winter 2018 0.992 0.067 0.360 0.447 2.391 0.527 3.631
baseline winter 2019 0.993 0.071 0.310 0.414 1.857 NaN NaN
elr winter 2019 1.000 0.071 0.292 0.422 1.710 0.517 3.261
baseline all 0.981 0.045 0.421 0.471 2.964 NaN NaN
elr all 0.990 0.054 0.337 0.436 2.532 0.509 3.308

Extended logistic regression plots

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