GMS location: 1404

Random forest results

names period power significance meanSquareError absError maxError CRPS IGN
baseline winter 2016 0.983 0.045 0.355 0.439 1.832 NaN NaN
forest winter 2016 0.995 0.045 0.241 0.372 1.546 0.453 5.810
baseline winter 2017 0.983 0.056 0.381 0.466 2.566 NaN NaN
forest winter 2017 0.991 0.111 0.258 0.379 1.582 0.452 5.718
baseline winter 2018 0.993 0.156 0.337 0.441 1.579 NaN NaN
forest winter 2018 0.985 0.125 0.265 0.386 1.920 0.456 5.894
baseline winter 2019 0.985 0.154 0.424 0.477 2.360 NaN NaN
forest winter 2019 0.992 0.154 0.294 0.395 1.817 0.445 6.125
baseline all 0.986 0.097 0.371 0.454 2.566 NaN NaN
forest all 0.991 0.107 0.262 0.382 1.920 0.452 5.879

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 0.045 0.355 0.439 1.832 NaN NaN
elr winter 2016 0.995 0.000e+00 0.275 0.403 1.861 0.524 4.894
baseline winter 2017 0.983 0.056 0.381 0.466 2.566 NaN NaN
elr winter 2017 0.983 0.083 0.268 0.397 2.032 0.517 4.569
baseline winter 2018 0.993 0.156 0.337 0.441 1.579 NaN NaN
elr winter 2018 0.985 0.125 0.300 0.419 2.364 0.541 4.673
baseline winter 2019 0.985 0.154 0.424 0.477 2.360 NaN NaN
elr winter 2019 0.992 0.154 0.317 0.434 1.759 0.493 4.109
baseline all 0.986 0.097 0.371 0.454 2.566 NaN NaN
elr all 0.989 0.087 0.289 0.412 2.364 0.520 4.594

Extended logistic regression plots

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