GMS location: 477

Random forest results

names period power significance meanSquareError absError maxError CRPS IGN
baseline winter 2016 0.970 0.083 0.531 0.532 3.523 NaN NaN
forest winter 2016 0.982 0.000e+00 0.265 0.378 2.347 0.429 3.809
baseline winter 2017 0.973 0.071 0.743 0.619 3.426 NaN NaN
forest winter 2017 0.991 0.095 0.398 0.459 3.110 0.442 4.394
baseline winter 2018 0.985 0.088 0.575 0.558 2.926 NaN NaN
forest winter 2018 0.985 0.176 0.317 0.428 1.764 0.438 3.743
baseline winter 2019 0.963 0.000e+00 0.566 0.525 3.154 NaN NaN
forest winter 2019 0.993 0.000e+00 0.237 0.356 1.527 0.424 3.226
baseline all 0.973 0.069 0.599 0.557 3.523 NaN NaN
forest all 0.987 0.086 0.302 0.404 3.110 0.433 3.792

Random forest plots

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Extended logistic regression results

names period power significance meanSquareError absError maxError CRPS IGN
baseline winter 2016 0.970 0.083 0.531 0.532 3.523 NaN NaN
elr winter 2016 0.970 0.000e+00 0.353 0.459 2.742 0.531 4.016
baseline winter 2017 0.973 0.071 0.743 0.619 3.426 NaN NaN
elr winter 2017 0.991 0.048 0.421 0.472 3.071 0.496 3.535
baseline winter 2018 0.985 0.088 0.575 0.558 2.926 NaN NaN
elr winter 2018 0.985 0.118 0.345 0.440 1.979 0.531 3.393
baseline winter 2019 0.963 0.000e+00 0.566 0.525 3.154 NaN NaN
elr winter 2019 0.993 0.062 0.307 0.420 1.771 0.523 3.607
baseline all 0.973 0.069 0.599 0.557 3.523 NaN NaN
elr all 0.984 0.060 0.356 0.448 3.071 0.521 3.655

Extended logistic regression plots

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