GMS location: 455

Random forest results

names period power significance meanSquareError absError maxError CRPS IGN
baseline winter 2016 0.965 0.105 0.534 0.534 2.995 NaN NaN
forest winter 2016 0.965 0.000e+00 0.335 0.422 2.264 0.452 4.720
baseline winter 2017 0.964 0.077 0.509 0.520 2.505 NaN NaN
forest winter 2017 0.964 0.077 0.272 0.409 1.722 0.443 2.583
baseline winter 2018 0.993 0.146 0.386 0.460 2.072 NaN NaN
forest winter 2018 0.993 0.146 0.348 0.414 2.509 0.439 2.911
baseline winter 2019 0.992 0.000e+00 0.364 0.446 2.013 NaN NaN
forest winter 2019 0.992 0.118 0.258 0.388 1.435 0.421 2.460
baseline all 0.978 0.095 0.452 0.492 2.995 NaN NaN
forest all 0.978 0.095 0.307 0.410 2.509 0.440 3.264

Random forest plots

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Extended logistic regression results

names period power significance meanSquareError absError maxError CRPS IGN
baseline winter 2016 0.965 0.105 0.534 0.534 2.995 NaN NaN
elr winter 2016 0.971 0.000e+00 0.403 0.473 2.424 0.515 3.441
baseline winter 2017 0.964 0.077 0.509 0.520 2.505 NaN NaN
elr winter 2017 0.955 0.077 0.315 0.418 1.940 0.485 2.821
baseline winter 2018 0.993 0.146 0.386 0.460 2.072 NaN NaN
elr winter 2018 0.993 0.122 0.375 0.453 2.287 0.534 3.913
baseline winter 2019 0.992 0.000e+00 0.364 0.446 2.013 NaN NaN
elr winter 2019 0.992 0.176 0.356 0.469 1.521 0.495 3.014
baseline all 0.978 0.095 0.452 0.492 2.995 NaN NaN
elr all 0.978 0.095 0.365 0.454 2.424 0.509 3.332

Extended logistic regression plots

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