GMS location: 414

Random forest results

names period power significance meanSquareError absError maxError CRPS IGN
baseline winter 2016 0.967 0.043 0.333 0.435 1.627 NaN NaN
forest winter 2016 0.973 0.043 0.295 0.400 1.709 0.503 4.537
baseline winter 2017 0.963 0.024 0.385 0.442 2.574 NaN NaN
forest winter 2017 0.973 0.073 0.287 0.377 2.093 0.475 3.381
baseline winter 2018 1.000 0.094 0.363 0.450 2.479 NaN NaN
forest winter 2018 1.000 0.094 0.304 0.425 2.358 0.482 4.097
baseline winter 2019 0.986 0.000e+00 0.310 0.381 2.507 NaN NaN
forest winter 2019 0.993 0.000e+00 0.236 0.349 1.766 0.474 2.850
baseline all 0.979 0.045 0.346 0.427 2.574 NaN NaN
forest all 0.984 0.063 0.281 0.389 2.358 0.485 3.776

Random forest plots

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Extended logistic regression results

names period power significance meanSquareError absError maxError CRPS IGN
baseline winter 2016 0.967 0.043 0.333 0.435 1.627 NaN NaN
elr winter 2016 0.973 0.000e+00 0.314 0.435 1.658 0.570 4.952
baseline winter 2017 0.963 0.024 0.385 0.442 2.574 NaN NaN
elr winter 2017 0.954 0.024 0.328 0.406 2.186 0.526 4.472
baseline winter 2018 1.000 0.094 0.363 0.450 2.479 NaN NaN
elr winter 2018 1.000 0.094 0.307 0.438 2.453 0.537 4.336
baseline winter 2019 0.986 0.000e+00 0.310 0.381 2.507 NaN NaN
elr winter 2019 0.993 0.000e+00 0.275 0.394 1.854 0.516 3.978
baseline all 0.979 0.045 0.346 0.427 2.574 NaN NaN
elr all 0.980 0.036 0.306 0.419 2.453 0.540 4.471

Extended logistic regression plots

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