GMS location: 507

Random forest results

names period power significance meanSquareError absError maxError CRPS IGN
baseline winter 2016 0.989 0.000e+00 0.327 0.444 1.700 NaN NaN
forest winter 2016 0.995 0.059 0.274 0.394 1.573 0.439 1.424
baseline winter 2017 0.984 0.000e+00 0.581 0.515 4.858 NaN NaN
forest winter 2017 0.968 0.000e+00 0.457 0.446 4.478 0.445 1.598
baseline winter 2018 0.980 0.100 0.470 0.475 2.957 NaN NaN
forest winter 2018 0.974 0.067 0.386 0.413 2.697 0.443 1.490
baseline winter 2019 0.993 0.077 0.627 0.469 4.203 NaN NaN
forest winter 2019 0.993 0.077 0.548 0.437 4.104 0.435 1.693
baseline all 0.987 0.044 0.488 0.473 4.858 NaN NaN
forest all 0.984 0.044 0.406 0.420 4.478 0.440 1.541

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 0.327 0.444 1.700 NaN NaN
elr winter 2016 0.995 0.000e+00 0.280 0.416 1.505 0.496 1.980
baseline winter 2017 0.984 0.000e+00 0.581 0.515 4.858 NaN NaN
elr winter 2017 0.976 0.000e+00 0.545 0.489 4.824 0.502 2.486
baseline winter 2018 0.980 0.100 0.470 0.475 2.957 NaN NaN
elr winter 2018 0.993 0.133 0.435 0.454 2.781 0.480 2.107
baseline winter 2019 0.993 0.077 0.627 0.469 4.203 NaN NaN
elr winter 2019 0.993 0.154 0.595 0.471 4.232 0.505 2.952
baseline all 0.987 0.044 0.488 0.473 4.858 NaN NaN
elr all 0.990 0.067 0.450 0.454 4.824 0.495 2.347

Extended logistic regression plots

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