GMS location: 555

Random forest results

names period power significance meanSquareError absError maxError CRPS IGN
baseline winter 2016 0.989 0.000e+00 0.366 0.472 1.644 NaN NaN
forest winter 2016 0.989 0.000e+00 0.300 0.423 1.728 0.471 2.611
baseline winter 2017 0.968 0.077 0.526 0.522 2.599 NaN NaN
forest winter 2017 0.976 0.077 0.429 0.459 2.469 0.484 2.844
baseline winter 2018 0.986 0.056 0.338 0.442 1.599 NaN NaN
forest winter 2018 0.986 0.167 0.286 0.398 1.747 0.480 2.384
baseline winter 2019 0.993 0.000e+00 0.293 0.377 2.525 NaN NaN
forest winter 2019 0.986 0.000e+00 0.255 0.368 2.154 0.467 2.255
baseline all 0.985 0.044 0.380 0.455 2.599 NaN NaN
forest all 0.985 0.073 0.316 0.413 2.469 0.475 2.530

Random forest plots

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Extended logistic regression results

names period power significance meanSquareError absError maxError CRPS IGN
baseline winter 2016 0.989 0.000e+00 0.366 0.472 1.644 NaN NaN
elr winter 2016 0.995 0.000e+00 0.345 0.458 1.983 0.540 4.026
baseline winter 2017 0.968 0.077 0.526 0.522 2.599 NaN NaN
elr winter 2017 0.968 0.115 0.491 0.515 2.152 0.515 4.085
baseline winter 2018 0.986 0.056 0.338 0.442 1.599 NaN NaN
elr winter 2018 0.978 0.167 0.299 0.410 2.003 0.517 3.331
baseline winter 2019 0.993 0.000e+00 0.293 0.377 2.525 NaN NaN
elr winter 2019 0.986 0.111 0.307 0.408 2.289 0.508 3.074
baseline all 0.985 0.044 0.380 0.455 2.599 NaN NaN
elr all 0.983 0.103 0.359 0.448 2.289 0.522 3.660

Extended logistic regression plots

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