GMS location: 215

Random forest results

names period power significance meanSquareError absError maxError CRPS IGN
baseline winter 2016 0.965 0.000e+00 0.391 0.480 1.987 NaN NaN
forest winter 2016 0.977 0.048 0.262 0.390 1.607 0.431 2.324
baseline winter 2017 0.975 0.061 0.468 0.517 2.315 NaN NaN
forest winter 2017 1.000 0.091 0.388 0.465 2.212 0.444 2.437
baseline winter 2018 1.000 NaN 0.486 0.456 2.359 NaN NaN
forest winter 2018 1.000 NaN 0.511 0.481 2.308 0.468 3.387
baseline winter 2019 1.000 NaN 0.218 0.353 1.161 NaN NaN
forest winter 2019 1.000 NaN 0.324 0.470 1.310 0.366 1.032
baseline all 0.975 0.037 0.424 0.485 2.359 NaN NaN
forest all 0.989 0.074 0.342 0.432 2.308 0.437 2.442

Random forest plots

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Extended logistic regression results

names period power significance meanSquareError absError maxError CRPS IGN
baseline winter 2016 0.965 0.000e+00 0.391 0.480 1.987 NaN NaN
elr winter 2016 0.965 0.000e+00 0.386 0.486 1.905 0.509 2.562
baseline winter 2017 0.975 0.061 0.468 0.517 2.315 NaN NaN
elr winter 2017 1.000 0.091 0.364 0.463 1.970 0.475 2.770
baseline winter 2018 1.000 NaN 0.486 0.456 2.359 NaN NaN
elr winter 2018 1.000 NaN 0.492 0.517 2.421 0.529 3.664
baseline winter 2019 1.000 NaN 0.218 0.353 1.161 NaN NaN
elr winter 2019 1.000 NaN 0.383 0.509 1.167 0.418 2.004
baseline all 0.975 0.037 0.424 0.485 2.359 NaN NaN
elr all 0.983 0.056 0.391 0.482 2.421 0.495 2.752

Extended logistic regression plots

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