GMS location: 558

Random forest results

names period power significance meanSquareError absError maxError CRPS IGN
baseline winter 2016 0.989 0.000e+00 0.349 0.447 2.201 NaN NaN
forest winter 2016 1.000 0.059 0.253 0.368 2.066 0.437 2.799
baseline winter 2017 0.968 0.033 0.462 0.527 2.029 NaN NaN
forest winter 2017 0.959 0.033 0.299 0.415 1.456 0.443 3.074
baseline winter 2018 0.994 0.080 0.301 0.435 1.576 NaN NaN
forest winter 2018 0.994 0.120 0.234 0.366 1.487 0.440 2.224
baseline winter 2019 1.000 0.071 0.376 0.456 2.044 NaN NaN
forest winter 2019 1.000 0.071 0.281 0.390 1.952 0.448 2.988
baseline all 0.989 0.046 0.368 0.464 2.201 NaN NaN
forest all 0.990 0.070 0.265 0.383 2.066 0.441 2.755

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 0.349 0.447 2.201 NaN NaN
elr winter 2016 0.995 0.059 0.282 0.413 2.127 0.506 4.280
baseline winter 2017 0.968 0.033 0.462 0.527 2.029 NaN NaN
elr winter 2017 0.968 0.033 0.321 0.437 1.596 0.537 4.618
baseline winter 2018 0.994 0.080 0.301 0.435 1.576 NaN NaN
elr winter 2018 0.987 0.120 0.285 0.404 1.766 0.518 4.171
baseline winter 2019 1.000 0.071 0.376 0.456 2.044 NaN NaN
elr winter 2019 1.000 0.143 0.336 0.436 1.836 0.526 5.142
baseline all 0.989 0.046 0.368 0.464 2.201 NaN NaN
elr all 0.989 0.081 0.304 0.421 2.127 0.520 4.522

Extended logistic regression plots

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