GMS location: 557
Random forest results
names |
period |
power |
significance |
meanSquareError |
absError |
maxError |
CRPS |
IGN |
baseline |
winter 2016 |
0.978 |
0.000e+00 |
0.353 |
0.467 |
1.817 |
NaN |
NaN |
forest |
winter 2016 |
0.989 |
0.000e+00 |
0.242 |
0.383 |
1.581 |
0.432 |
2.748 |
baseline |
winter 2017 |
0.968 |
0.000e+00 |
0.453 |
0.515 |
2.099 |
NaN |
NaN |
forest |
winter 2017 |
0.976 |
0.000e+00 |
0.292 |
0.404 |
1.795 |
0.443 |
3.354 |
baseline |
winter 2018 |
0.985 |
0.115 |
0.378 |
0.478 |
2.081 |
NaN |
NaN |
forest |
winter 2018 |
0.985 |
0.115 |
0.302 |
0.405 |
1.886 |
0.443 |
2.852 |
baseline |
winter 2019 |
0.978 |
0.000e+00 |
0.343 |
0.436 |
1.957 |
NaN |
NaN |
forest |
winter 2019 |
0.986 |
0.000e+00 |
0.286 |
0.399 |
1.863 |
0.442 |
2.751 |
baseline |
all |
0.978 |
0.037 |
0.380 |
0.474 |
2.099 |
NaN |
NaN |
forest |
all |
0.985 |
0.037 |
0.278 |
0.397 |
1.886 |
0.439 |
2.914 |
Random forest plots
Extended logistic regression results
names |
period |
power |
significance |
meanSquareError |
absError |
maxError |
CRPS |
IGN |
baseline |
winter 2016 |
0.978 |
0.000e+00 |
0.353 |
0.467 |
1.817 |
NaN |
NaN |
elr |
winter 2016 |
0.995 |
0.000e+00 |
0.269 |
0.409 |
1.742 |
0.496 |
3.842 |
baseline |
winter 2017 |
0.968 |
0.000e+00 |
0.453 |
0.515 |
2.099 |
NaN |
NaN |
elr |
winter 2017 |
0.976 |
0.000e+00 |
0.359 |
0.451 |
1.823 |
0.498 |
4.564 |
baseline |
winter 2018 |
0.985 |
0.115 |
0.378 |
0.478 |
2.081 |
NaN |
NaN |
elr |
winter 2018 |
0.985 |
0.077 |
0.305 |
0.412 |
2.031 |
0.512 |
5.045 |
baseline |
winter 2019 |
0.978 |
0.000e+00 |
0.343 |
0.436 |
1.957 |
NaN |
NaN |
elr |
winter 2019 |
0.986 |
0.000e+00 |
0.340 |
0.453 |
2.122 |
0.533 |
5.183 |
baseline |
all |
0.978 |
0.037 |
0.380 |
0.474 |
2.099 |
NaN |
NaN |
elr |
all |
0.986 |
0.025 |
0.315 |
0.429 |
2.122 |
0.509 |
4.604 |
Extended logistic regression plots