GMS location: 510

Random forest results

names period power significance meanSquareError absError maxError CRPS IGN
baseline winter 2016 0.984 0.056 0.298 0.407 2.301 NaN NaN
forest winter 2016 1.000 0.000e+00 0.266 0.382 2.197 0.498 4.233
baseline winter 2017 0.975 0.043 0.349 0.446 2.222 NaN NaN
forest winter 2017 0.983 0.043 0.273 0.376 2.101 0.512 4.779
baseline winter 2018 0.993 0.080 0.329 0.420 2.005 NaN NaN
forest winter 2018 0.986 0.160 0.299 0.408 1.851 0.517 4.527
baseline winter 2019 0.979 0.000e+00 0.227 0.368 1.699 NaN NaN
forest winter 2019 0.986 0.000e+00 0.197 0.335 1.747 0.505 3.674
baseline all 0.983 0.053 0.300 0.409 2.301 NaN NaN
forest all 0.990 0.066 0.259 0.376 2.197 0.507 4.291

Random forest plots

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Extended logistic regression results

names period power significance meanSquareError absError maxError CRPS IGN
baseline winter 2016 0.984 0.056 0.298 0.407 2.301 NaN NaN
elr winter 2016 0.989 0.000e+00 0.275 0.404 2.059 0.569 5.938
baseline winter 2017 0.975 0.043 0.349 0.446 2.222 NaN NaN
elr winter 2017 0.975 0.043 0.297 0.403 2.287 0.532 5.715
baseline winter 2018 0.993 0.080 0.329 0.420 2.005 NaN NaN
elr winter 2018 0.986 0.080 0.310 0.411 1.994 0.576 7.980
baseline winter 2019 0.979 0.000e+00 0.227 0.368 1.699 NaN NaN
elr winter 2019 0.986 0.000e+00 0.194 0.337 1.435 0.542 4.804
baseline all 0.983 0.053 0.300 0.409 2.301 NaN NaN
elr all 0.985 0.040 0.269 0.390 2.287 0.556 6.131

Extended logistic regression plots

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