GMS location: 518

Random forest results

names period power significance meanSquareError absError maxError CRPS IGN
baseline winter 2016 0.973 0.000e+00 0.384 0.471 1.939 NaN NaN
forest winter 2016 1.000 0.000e+00 0.264 0.389 2.046 0.431 3.010
baseline winter 2017 0.977 0.040 0.499 0.518 2.457 NaN NaN
forest winter 2017 0.984 0.040 0.296 0.388 1.776 0.436 3.235
baseline winter 2018 0.981 0.045 0.295 0.416 2.054 NaN NaN
forest winter 2018 0.994 0.045 0.226 0.352 1.946 0.443 2.445
baseline winter 2019 0.974 0.111 0.329 0.418 1.990 NaN NaN
forest winter 2019 0.980 0.111 0.249 0.374 1.595 0.443 2.564
baseline all 0.976 0.043 0.373 0.455 2.457 NaN NaN
forest all 0.990 0.043 0.258 0.376 2.046 0.438 2.810

Random forest plots

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Extended logistic regression results

names period power significance meanSquareError absError maxError CRPS IGN
baseline winter 2016 0.973 0.000e+00 0.384 0.471 1.939 NaN NaN
elr winter 2016 0.984 0.000e+00 0.303 0.433 2.164 0.483 4.128
baseline winter 2017 0.977 0.040 0.499 0.518 2.457 NaN NaN
elr winter 2017 0.977 0.040 0.393 0.456 2.054 0.515 5.331
baseline winter 2018 0.981 0.045 0.295 0.416 2.054 NaN NaN
elr winter 2018 0.987 0.045 0.268 0.394 2.242 0.507 4.095
baseline winter 2019 0.974 0.111 0.329 0.418 1.990 NaN NaN
elr winter 2019 0.987 0.222 0.270 0.402 1.502 0.497 4.266
baseline all 0.976 0.043 0.373 0.455 2.457 NaN NaN
elr all 0.984 0.057 0.306 0.421 2.242 0.500 4.417

Extended logistic regression plots

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