GMS location: 203

Random forest results

names period power significance meanSquareError absError maxError CRPS IGN
baseline winter 2016 0.984 0.000e+00 0.445 0.507 2.897 NaN NaN
forest winter 2016 0.989 0.000e+00 0.342 0.441 2.656 0.463 4.781
baseline winter 2017 0.967 0.094 0.486 0.528 2.411 NaN NaN
forest winter 2017 0.975 0.094 0.315 0.425 1.527 0.476 3.545
baseline winter 2018 0.986 0.133 0.324 0.429 1.923 NaN NaN
forest winter 2018 0.993 0.133 0.269 0.375 2.136 0.458 2.454
baseline winter 2019 0.993 0.077 0.375 0.455 2.413 NaN NaN
forest winter 2019 1.000 0.077 0.287 0.405 2.322 0.451 2.801
baseline all 0.983 0.087 0.408 0.480 2.897 NaN NaN
forest all 0.990 0.087 0.305 0.412 2.656 0.462 3.473

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 0.000e+00 0.445 0.507 2.897 NaN NaN
elr winter 2016 0.984 0.000e+00 0.363 0.463 2.380 0.551 4.864
baseline winter 2017 0.967 0.094 0.486 0.528 2.411 NaN NaN
elr winter 2017 0.975 0.125 0.335 0.442 1.769 0.529 4.181
baseline winter 2018 0.986 0.133 0.324 0.429 1.923 NaN NaN
elr winter 2018 0.993 0.133 0.298 0.411 2.228 0.530 3.590
baseline winter 2019 0.993 0.077 0.375 0.455 2.413 NaN NaN
elr winter 2019 1.000 0.077 0.309 0.420 2.077 0.531 4.110
baseline all 0.983 0.087 0.408 0.480 2.897 NaN NaN
elr all 0.988 0.098 0.328 0.435 2.380 0.536 4.219

Extended logistic regression plots

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