GMS location: 372

Random forest results

names period power significance meanSquareError absError maxError CRPS IGN
baseline winter 2016 0.995 0.000e+00 0.295 0.412 2.020 NaN NaN
forest winter 2016 1.000 0.154 0.288 0.404 2.010 0.449 5.856
baseline winter 2017 0.962 0.000e+00 0.364 0.434 2.504 NaN NaN
forest winter 2017 0.985 0.000e+00 0.275 0.384 1.833 0.467 4.235
baseline winter 2018 0.974 0.062 0.270 0.406 1.603 NaN NaN
forest winter 2018 0.974 0.125 0.214 0.350 1.572 0.477 3.767
baseline winter 2019 0.993 0.167 0.263 0.372 2.127 NaN NaN
forest winter 2019 0.993 0.167 0.214 0.338 2.006 0.481 4.235
baseline all 0.982 0.035 0.298 0.407 2.504 NaN NaN
forest all 0.989 0.088 0.251 0.372 2.010 0.467 4.617

Random forest plots

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Extended logistic regression results

names period power significance meanSquareError absError maxError CRPS IGN
baseline winter 2016 0.995 0.000e+00 0.295 0.412 2.020 NaN NaN
elr winter 2016 1.000 0.077 0.300 0.419 1.884 0.513 5.885
baseline winter 2017 0.962 0.000e+00 0.364 0.434 2.504 NaN NaN
elr winter 2017 0.970 0.000e+00 0.334 0.432 2.246 0.549 7.454
baseline winter 2018 0.974 0.062 0.270 0.406 1.603 NaN NaN
elr winter 2018 0.980 0.062 0.227 0.366 1.581 0.530 5.042
baseline winter 2019 0.993 0.167 0.263 0.372 2.127 NaN NaN
elr winter 2019 0.993 0.000e+00 0.240 0.368 2.164 0.548 5.688
baseline all 0.982 0.035 0.298 0.407 2.504 NaN NaN
elr all 0.987 0.035 0.277 0.398 2.246 0.533 5.992

Extended logistic regression plots

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