GMS location: 457

Random forest results

names period power significance meanSquareError absError maxError CRPS IGN
baseline winter 2016 0.983 0.000e+00 0.478 0.532 2.035 NaN NaN
forest winter 2016 0.994 0.038 0.373 0.454 1.989 0.488 3.573
baseline winter 2017 0.973 0.045 0.431 0.478 2.320 NaN NaN
forest winter 2017 0.963 0.068 0.304 0.410 1.788 0.474 2.831
baseline winter 2018 0.985 0.175 0.409 0.468 2.467 NaN NaN
forest winter 2018 0.977 0.150 0.323 0.394 2.260 0.476 2.532
baseline winter 2019 1.000 0.059 0.283 0.398 1.560 NaN NaN
forest winter 2019 1.000 0.059 0.195 0.329 1.344 0.460 2.158
baseline all 0.985 0.079 0.408 0.475 2.467 NaN NaN
forest all 0.985 0.087 0.306 0.402 2.260 0.476 2.834

Random forest plots

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Extended logistic regression results

names period power significance meanSquareError absError maxError CRPS IGN
baseline winter 2016 0.983 0.000e+00 0.478 0.532 2.035 NaN NaN
elr winter 2016 0.994 0.038 0.425 0.493 1.969 0.544 4.454
baseline winter 2017 0.973 0.045 0.431 0.478 2.320 NaN NaN
elr winter 2017 0.973 0.068 0.373 0.469 1.951 0.510 3.196
baseline winter 2018 0.985 0.175 0.409 0.468 2.467 NaN NaN
elr winter 2018 0.977 0.175 0.333 0.419 2.444 0.525 3.447
baseline winter 2019 1.000 0.059 0.283 0.398 1.560 NaN NaN
elr winter 2019 1.000 0.118 0.224 0.355 1.398 0.497 2.739
baseline all 0.985 0.079 0.408 0.475 2.467 NaN NaN
elr all 0.987 0.102 0.346 0.439 2.444 0.521 3.543

Extended logistic regression plots

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