GMS location: 1409

Random forest results

names period power significance meanSquareError absError maxError CRPS IGN
baseline winter 2016 0.971 0.000e+00 0.415 0.474 2.307 NaN NaN
forest winter 2016 0.982 0.000e+00 0.280 0.379 2.279 0.444 4.069
baseline winter 2017 0.962 0.027 0.577 0.544 2.239 NaN NaN
forest winter 2017 0.971 0.054 0.351 0.431 1.746 0.436 3.661
baseline winter 2018 1.000 0.077 0.371 0.450 1.966 NaN NaN
forest winter 2018 0.992 0.103 0.291 0.399 1.930 0.441 3.322
baseline winter 2019 0.978 0.000e+00 0.343 0.448 1.760 NaN NaN
forest winter 2019 0.993 0.000e+00 0.199 0.339 1.313 0.430 2.729
baseline all 0.978 0.037 0.423 0.477 2.307 NaN NaN
forest all 0.985 0.055 0.280 0.386 2.279 0.438 3.484

Random forest plots

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Extended logistic regression results

names period power significance meanSquareError absError maxError CRPS IGN
baseline winter 2016 0.971 0.000e+00 0.415 0.474 2.307 NaN NaN
elr winter 2016 0.953 0.000e+00 0.347 0.428 2.407 0.535 4.625
baseline winter 2017 0.962 0.027 0.577 0.544 2.239 NaN NaN
elr winter 2017 0.971 0.081 0.430 0.492 2.092 0.509 4.503
baseline winter 2018 1.000 0.077 0.371 0.450 1.966 NaN NaN
elr winter 2018 0.992 0.103 0.312 0.430 1.867 0.526 4.201
baseline winter 2019 0.978 0.000e+00 0.343 0.448 1.760 NaN NaN
elr winter 2019 0.993 0.000e+00 0.248 0.386 1.524 0.507 3.507
baseline all 0.978 0.037 0.423 0.477 2.307 NaN NaN
elr all 0.976 0.064 0.334 0.433 2.407 0.521 4.236

Extended logistic regression plots

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