GMS location: 717

Random forest results

names period power significance meanSquareError absError maxError CRPS IGN
baseline winter 2016 0.994 0.150 0.764 0.593 3.503 NaN NaN
forest winter 2016 0.981 0.200 0.741 0.572 3.636 0.452 1.592
baseline winter 2017 0.968 0.111 0.802 0.546 5.899 NaN NaN
forest winter 2017 0.984 0.111 0.723 0.499 5.552 0.450 1.482
baseline winter 2018 1.000 0.000e+00 0.397 0.488 1.455 NaN NaN
forest winter 2018 1.000 0.000e+00 0.287 0.389 1.579 0.410 1.218
baseline winter 2019 0.979 0.083 0.417 0.447 2.806 NaN NaN
forest winter 2019 0.986 0.083 0.406 0.417 2.885 0.442 1.230
baseline all 0.983 0.117 0.642 0.527 5.899 NaN NaN
forest all 0.985 0.133 0.600 0.490 5.552 0.445 1.423

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 0.150 0.764 0.593 3.503 NaN NaN
elr winter 2016 0.994 0.150 0.662 0.542 3.582 0.462 1.549
baseline winter 2017 0.968 0.111 0.802 0.546 5.899 NaN NaN
elr winter 2017 0.976 0.111 0.722 0.508 5.833 0.507 1.873
baseline winter 2018 1.000 0.000e+00 0.397 0.488 1.455 NaN NaN
elr winter 2018 1.000 0.000e+00 0.274 0.355 1.526 0.417 1.213
baseline winter 2019 0.979 0.083 0.417 0.447 2.806 NaN NaN
elr winter 2019 0.986 0.083 0.412 0.443 2.922 0.448 1.296
baseline all 0.983 0.117 0.642 0.527 5.899 NaN NaN
elr all 0.987 0.117 0.574 0.488 5.833 0.467 1.540

Extended logistic regression plots

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