GMS location: 811

Random forest results

names period power significance meanSquareError absError maxError CRPS IGN
baseline winter 2016 0.978 0.000e+00 0.392 0.444 2.834 NaN NaN
forest winter 2016 0.962 0.048 0.389 0.453 2.498 0.535 3.178
baseline winter 2017 0.991 0.108 0.305 0.398 1.901 NaN NaN
forest winter 2017 0.991 0.135 0.284 0.394 1.987 0.510 2.858
baseline winter 2018 0.987 0.161 0.441 0.481 2.230 NaN NaN
forest winter 2018 0.980 0.129 0.352 0.432 2.376 0.526 2.985
baseline winter 2019 0.985 0.231 0.401 0.475 2.159 NaN NaN
forest winter 2019 0.985 0.231 0.321 0.418 2.311 0.520 2.499
baseline all 0.985 0.130 0.388 0.451 2.834 NaN NaN
forest all 0.978 0.139 0.341 0.427 2.498 0.524 2.905

Random forest plots

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Extended logistic regression results

names period power significance meanSquareError absError maxError CRPS IGN
baseline winter 2016 0.978 0.000e+00 0.392 0.444 2.834 NaN NaN
elr winter 2016 0.962 0.000e+00 0.418 0.485 2.646 0.616 4.015
baseline winter 2017 0.991 0.108 0.305 0.398 1.901 NaN NaN
elr winter 2017 1.000 0.054 0.292 0.404 2.135 0.559 2.991
baseline winter 2018 0.987 0.161 0.441 0.481 2.230 NaN NaN
elr winter 2018 0.980 0.097 0.394 0.458 2.308 0.559 3.426
baseline winter 2019 0.985 0.231 0.401 0.475 2.159 NaN NaN
elr winter 2019 0.985 0.154 0.339 0.439 2.570 0.560 3.038
baseline all 0.985 0.130 0.388 0.451 2.834 NaN NaN
elr all 0.979 0.078 0.366 0.450 2.646 0.576 3.418

Extended logistic regression plots

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