GMS location: 516

Random forest results

names period power significance meanSquareError absError maxError CRPS IGN
baseline winter 2016 0.989 0.100 0.321 0.436 1.916 NaN NaN
forest winter 2016 0.989 0.100 0.254 0.386 1.722 0.520 4.592
baseline winter 2017 0.933 0.189 0.436 0.476 2.199 NaN NaN
forest winter 2017 0.933 0.162 0.335 0.416 1.963 0.515 5.062
baseline winter 2018 0.986 0.057 0.344 0.421 1.830 NaN NaN
forest winter 2018 0.986 0.057 0.287 0.398 1.633 0.514 3.206
baseline winter 2019 1.000 0.000e+00 0.309 0.429 2.299 NaN NaN
forest winter 2019 0.993 0.000e+00 0.240 0.377 1.959 0.526 3.928
baseline all 0.982 0.104 0.346 0.438 2.299 NaN NaN
forest all 0.980 0.094 0.275 0.393 1.963 0.519 4.150

Random forest plots

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Extended logistic regression results

names period power significance meanSquareError absError maxError CRPS IGN
baseline winter 2016 0.989 0.100 0.321 0.436 1.916 NaN NaN
elr winter 2016 0.983 0.100 0.310 0.435 1.636 0.603 5.410
baseline winter 2017 0.933 0.189 0.436 0.476 2.199 NaN NaN
elr winter 2017 0.944 0.135 0.369 0.440 2.196 0.584 5.400
baseline winter 2018 0.986 0.057 0.344 0.421 1.830 NaN NaN
elr winter 2018 0.979 0.143 0.327 0.438 1.661 0.571 4.934
baseline winter 2019 1.000 0.000e+00 0.309 0.429 2.299 NaN NaN
elr winter 2019 0.993 0.071 0.272 0.405 1.933 0.565 4.454
baseline all 0.982 0.104 0.346 0.438 2.299 NaN NaN
elr all 0.978 0.123 0.317 0.430 2.196 0.582 5.052

Extended logistic regression plots

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