GMS location: 253

Random forest results

names period power significance meanSquareError absError maxError CRPS IGN
baseline winter 2016 0.993 0.000e+00 0.304 0.433 1.571 NaN NaN
forest winter 2016 0.993 0.000e+00 0.226 0.377 1.421 0.482 7.228
baseline winter 2017 0.958 0.061 0.373 0.452 1.890 NaN NaN
forest winter 2017 0.958 0.000e+00 0.263 0.379 1.439 0.477 7.086
baseline winter 2018 0.985 0.259 0.285 0.396 1.833 NaN NaN
forest winter 2018 0.985 0.222 0.222 0.352 1.976 0.503 4.921
baseline winter 2019 0.993 0.000e+00 0.235 0.351 1.647 NaN NaN
forest winter 2019 0.993 0.067 0.205 0.324 1.633 0.479 5.289
baseline all 0.983 0.097 0.299 0.408 1.890 NaN NaN
forest all 0.983 0.075 0.229 0.358 1.976 0.485 6.112

Random forest plots

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Extended logistic regression results

names period power significance meanSquareError absError maxError CRPS IGN
baseline winter 2016 0.993 0.000e+00 0.304 0.433 1.571 NaN NaN
elr winter 2016 0.993 0.000e+00 0.261 0.410 1.317 0.558 6.375
baseline winter 2017 0.958 0.061 0.373 0.452 1.890 NaN NaN
elr winter 2017 0.967 0.030 0.285 0.403 1.647 0.542 6.156
baseline winter 2018 0.985 0.259 0.285 0.396 1.833 NaN NaN
elr winter 2018 0.985 0.222 0.261 0.385 2.091 0.536 5.370
baseline winter 2019 0.993 0.000e+00 0.235 0.351 1.647 NaN NaN
elr winter 2019 0.993 0.067 0.233 0.356 1.696 0.496 4.371
baseline all 0.983 0.097 0.299 0.408 1.890 NaN NaN
elr all 0.985 0.086 0.260 0.389 2.091 0.533 5.559

Extended logistic regression plots

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