GMS location: 1435

Random forest results

names period power significance meanSquareError absError maxError CRPS IGN
baseline winter 2016 0.994 0.032 0.334 0.414 3.400 NaN NaN
forest winter 2016 0.981 0.000e+00 0.296 0.383 3.084 0.472 3.885
baseline winter 2017 0.982 0.093 0.392 0.436 2.470 NaN NaN
forest winter 2017 0.982 0.070 0.327 0.417 2.095 0.462 2.636
baseline winter 2018 0.993 0.083 0.430 0.498 2.146 NaN NaN
forest winter 2018 0.993 0.028 0.332 0.439 1.953 0.472 2.872
baseline winter 2019 0.984 0.048 0.391 0.459 2.046 NaN NaN
forest winter 2019 1.000 0.143 0.280 0.391 1.736 0.464 3.097
baseline all 0.989 0.069 0.385 0.451 3.400 NaN NaN
forest all 0.989 0.053 0.309 0.407 3.084 0.468 3.158

Random forest plots

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Extended logistic regression results

names period power significance meanSquareError absError maxError CRPS IGN
baseline winter 2016 0.994 0.032 0.334 0.414 3.400 NaN NaN
elr winter 2016 0.981 0.032 0.323 0.415 3.230 0.520 3.231
baseline winter 2017 0.982 0.093 0.392 0.436 2.470 NaN NaN
elr winter 2017 0.982 0.046 0.367 0.450 2.225 0.537 4.185
baseline winter 2018 0.993 0.083 0.430 0.498 2.146 NaN NaN
elr winter 2018 0.978 0.000e+00 0.415 0.493 2.003 0.545 3.987
baseline winter 2019 0.984 0.048 0.391 0.459 2.046 NaN NaN
elr winter 2019 1.000 0.095 0.320 0.439 1.884 0.508 3.115
baseline all 0.989 0.069 0.385 0.451 3.400 NaN NaN
elr all 0.985 0.038 0.356 0.449 3.230 0.528 3.620

Extended logistic regression plots

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