GMS location: 421
Random forest results
names |
period |
power |
significance |
meanSquareError |
absError |
maxError |
CRPS |
IGN |
baseline |
winter 2016 |
0.982 |
0.154 |
0.313 |
0.406 |
2.099 |
NaN |
NaN |
forest |
winter 2016 |
0.982 |
0.154 |
0.263 |
0.361 |
2.027 |
0.493 |
2.982 |
baseline |
winter 2017 |
0.970 |
0.048 |
0.496 |
0.491 |
3.043 |
NaN |
NaN |
forest |
winter 2017 |
0.960 |
0.048 |
0.356 |
0.429 |
2.041 |
0.483 |
2.970 |
baseline |
winter 2018 |
0.989 |
0.083 |
0.379 |
0.405 |
3.115 |
NaN |
NaN |
forest |
winter 2018 |
0.979 |
0.056 |
0.326 |
0.394 |
3.029 |
0.492 |
3.478 |
baseline |
winter 2019 |
0.990 |
0.000e+00 |
0.342 |
0.397 |
2.355 |
NaN |
NaN |
forest |
winter 2019 |
0.990 |
0.050 |
0.234 |
0.352 |
1.661 |
0.493 |
2.599 |
baseline |
all |
0.983 |
0.073 |
0.378 |
0.424 |
3.115 |
NaN |
NaN |
forest |
all |
0.978 |
0.073 |
0.293 |
0.383 |
3.029 |
0.490 |
3.008 |
Random forest plots
Extended logistic regression results
names |
period |
power |
significance |
meanSquareError |
absError |
maxError |
CRPS |
IGN |
baseline |
winter 2016 |
0.982 |
0.154 |
0.313 |
0.406 |
2.099 |
NaN |
NaN |
elr |
winter 2016 |
0.982 |
0.077 |
0.270 |
0.389 |
1.923 |
0.566 |
3.384 |
baseline |
winter 2017 |
0.970 |
0.048 |
0.496 |
0.491 |
3.043 |
NaN |
NaN |
elr |
winter 2017 |
0.970 |
0.024 |
0.343 |
0.443 |
1.973 |
0.531 |
3.218 |
baseline |
winter 2018 |
0.989 |
0.083 |
0.379 |
0.405 |
3.115 |
NaN |
NaN |
elr |
winter 2018 |
0.979 |
0.028 |
0.337 |
0.404 |
2.883 |
0.541 |
3.904 |
baseline |
winter 2019 |
0.990 |
0.000e+00 |
0.342 |
0.397 |
2.355 |
NaN |
NaN |
elr |
winter 2019 |
0.990 |
0.150 |
0.291 |
0.408 |
1.702 |
0.565 |
3.464 |
baseline |
all |
0.983 |
0.073 |
0.378 |
0.424 |
3.115 |
NaN |
NaN |
elr |
all |
0.981 |
0.057 |
0.307 |
0.409 |
2.883 |
0.552 |
3.476 |
Extended logistic regression plots