GMS location: 905

Random forest results

names period power significance meanSquareError absError maxError CRPS IGN
baseline winter 2016 0.979 0.000e+00 0.317 0.417 1.772 NaN NaN
forest winter 2016 0.984 0.067 0.271 0.393 1.515 0.482 3.683
baseline winter 2017 0.956 0.028 0.375 0.457 2.498 NaN NaN
forest winter 2017 0.933 0.083 0.308 0.412 1.885 0.473 4.258
baseline winter 2018 0.986 0.172 0.366 0.459 2.066 NaN NaN
forest winter 2018 0.986 0.241 0.336 0.427 1.845 0.489 3.223
baseline winter 2019 0.986 0.000e+00 0.283 0.384 1.969 NaN NaN
forest winter 2019 0.986 0.059 0.235 0.348 1.600 0.470 2.887
baseline all 0.979 0.062 0.332 0.427 2.498 NaN NaN
forest all 0.977 0.124 0.286 0.395 1.885 0.479 3.477

Random forest plots

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Extended logistic regression results

names period power significance meanSquareError absError maxError CRPS IGN
baseline winter 2016 0.979 0.000e+00 0.317 0.417 1.772 NaN NaN
elr winter 2016 0.990 0.000e+00 0.291 0.410 1.695 0.532 3.885
baseline winter 2017 0.956 0.028 0.375 0.457 2.498 NaN NaN
elr winter 2017 0.944 0.056 0.339 0.436 2.160 0.542 4.777
baseline winter 2018 0.986 0.172 0.366 0.459 2.066 NaN NaN
elr winter 2018 0.986 0.172 0.351 0.449 2.274 0.516 4.214
baseline winter 2019 0.986 0.000e+00 0.283 0.384 1.969 NaN NaN
elr winter 2019 0.993 0.118 0.249 0.373 1.481 0.491 3.097
baseline all 0.979 0.062 0.332 0.427 2.498 NaN NaN
elr all 0.983 0.093 0.305 0.416 2.274 0.520 3.947

Extended logistic regression plots

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