GMS location: 841

Random forest results

names period power significance meanSquareError absError maxError CRPS IGN
baseline winter 2016 0.972 0.000e+00 0.306 0.381 2.300 NaN NaN
forest winter 2016 0.989 0.067 0.282 0.361 2.166 0.456 3.290
baseline winter 2017 0.983 0.088 0.344 0.412 2.200 NaN NaN
forest winter 2017 0.983 0.088 0.295 0.383 1.823 0.432 2.347
baseline winter 2018 0.980 0.074 0.429 0.455 3.126 NaN NaN
forest winter 2018 0.980 0.111 0.411 0.455 3.336 0.446 3.077
baseline winter 2019 0.979 0.000e+00 0.316 0.399 2.402 NaN NaN
forest winter 2019 0.986 0.118 0.266 0.388 1.636 0.442 2.156
baseline all 0.978 0.054 0.349 0.411 3.126 NaN NaN
forest all 0.985 0.097 0.315 0.397 3.336 0.445 2.757

Random forest plots

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Extended logistic regression results

names period power significance meanSquareError absError maxError CRPS IGN
baseline winter 2016 0.972 0.000e+00 0.306 0.381 2.300 NaN NaN
elr winter 2016 0.960 0.000e+00 0.322 0.406 2.125 0.514 3.300
baseline winter 2017 0.983 0.088 0.344 0.412 2.200 NaN NaN
elr winter 2017 0.983 0.118 0.275 0.389 1.843 0.477 2.610
baseline winter 2018 0.980 0.074 0.429 0.455 3.126 NaN NaN
elr winter 2018 0.980 0.111 0.392 0.446 3.567 0.480 3.352
baseline winter 2019 0.979 0.000e+00 0.316 0.399 2.402 NaN NaN
elr winter 2019 0.979 0.118 0.242 0.376 1.321 0.457 2.273
baseline all 0.978 0.054 0.349 0.411 3.126 NaN NaN
elr all 0.974 0.097 0.311 0.406 3.567 0.484 2.918

Extended logistic regression plots

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