GMS location: 1172

Random forest results

names period power significance meanSquareError absError maxError CRPS IGN
baseline winter 2016 0.994 0.091 0.509 0.478 3.786 NaN NaN
forest winter 2016 0.989 0.030 0.420 0.441 3.540 0.536 2.454
baseline winter 2017 0.983 0.083 0.551 0.529 2.623 NaN NaN
forest winter 2017 0.991 0.056 0.421 0.463 2.299 0.544 2.806
baseline winter 2018 0.993 0.114 0.390 0.460 2.097 NaN NaN
forest winter 2018 0.970 0.068 0.372 0.449 2.373 0.556 2.202
baseline winter 2019 0.993 0.167 0.422 0.477 1.943 NaN NaN
forest winter 2019 1.000 0.167 0.298 0.401 1.787 0.535 2.384
baseline all 0.991 0.104 0.468 0.484 3.786 NaN NaN
forest all 0.988 0.064 0.381 0.439 3.540 0.543 2.451

Random forest plots

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Extended logistic regression results

names period power significance meanSquareError absError maxError CRPS IGN
baseline winter 2016 0.994 0.091 0.509 0.478 3.786 NaN NaN
elr winter 2016 0.989 0.030 0.481 0.487 4.215 0.607 3.216
baseline winter 2017 0.983 0.083 0.551 0.529 2.623 NaN NaN
elr winter 2017 0.983 0.056 0.501 0.510 2.471 0.571 2.888
baseline winter 2018 0.993 0.114 0.390 0.460 2.097 NaN NaN
elr winter 2018 0.985 0.068 0.444 0.489 2.841 0.612 2.942
baseline winter 2019 0.993 0.167 0.422 0.477 1.943 NaN NaN
elr winter 2019 1.000 0.167 0.335 0.434 1.787 0.593 2.733
baseline all 0.991 0.104 0.468 0.484 3.786 NaN NaN
elr all 0.989 0.064 0.444 0.481 4.215 0.597 2.967

Extended logistic regression plots

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