GMS location: 601
Random forest results
names |
period |
power |
significance |
meanSquareError |
absError |
maxError |
CRPS |
IGN |
baseline |
winter 2016 |
1.000 |
0.000e+00 |
0.359 |
0.427 |
2.632 |
NaN |
NaN |
forest |
winter 2016 |
0.995 |
0.000e+00 |
0.310 |
0.417 |
2.935 |
0.557 |
3.134 |
baseline |
winter 2017 |
0.991 |
0.057 |
0.528 |
0.514 |
2.894 |
NaN |
NaN |
forest |
winter 2017 |
0.982 |
0.057 |
0.448 |
0.467 |
2.834 |
0.533 |
3.960 |
baseline |
winter 2018 |
0.985 |
0.176 |
0.422 |
0.476 |
2.540 |
NaN |
NaN |
forest |
winter 2018 |
0.985 |
0.176 |
0.338 |
0.431 |
2.248 |
0.546 |
3.224 |
baseline |
winter 2019 |
0.994 |
0.000e+00 |
0.315 |
0.414 |
2.124 |
NaN |
NaN |
forest |
winter 2019 |
1.000 |
0.000e+00 |
0.236 |
0.344 |
1.812 |
0.529 |
2.377 |
baseline |
all |
0.993 |
0.082 |
0.400 |
0.455 |
2.894 |
NaN |
NaN |
forest |
all |
0.991 |
0.082 |
0.329 |
0.413 |
2.935 |
0.542 |
3.150 |
Random forest plots
Extended logistic regression results
names |
period |
power |
significance |
meanSquareError |
absError |
maxError |
CRPS |
IGN |
baseline |
winter 2016 |
1.000 |
0.000e+00 |
0.359 |
0.427 |
2.632 |
NaN |
NaN |
elr |
winter 2016 |
0.984 |
0.000e+00 |
0.357 |
0.448 |
3.139 |
0.617 |
4.807 |
baseline |
winter 2017 |
0.991 |
0.057 |
0.528 |
0.514 |
2.894 |
NaN |
NaN |
elr |
winter 2017 |
0.973 |
0.057 |
0.491 |
0.499 |
2.984 |
0.585 |
5.139 |
baseline |
winter 2018 |
0.985 |
0.176 |
0.422 |
0.476 |
2.540 |
NaN |
NaN |
elr |
winter 2018 |
0.985 |
0.176 |
0.385 |
0.475 |
2.546 |
0.606 |
4.403 |
baseline |
winter 2019 |
0.994 |
0.000e+00 |
0.315 |
0.414 |
2.124 |
NaN |
NaN |
elr |
winter 2019 |
0.994 |
0.000e+00 |
0.254 |
0.358 |
2.040 |
0.579 |
3.468 |
baseline |
all |
0.993 |
0.082 |
0.400 |
0.455 |
2.894 |
NaN |
NaN |
elr |
all |
0.985 |
0.082 |
0.368 |
0.443 |
3.139 |
0.598 |
4.454 |
Extended logistic regression plots