GMS location: 456
Random forest results
names |
period |
power |
significance |
meanSquareError |
absError |
maxError |
CRPS |
IGN |
baseline |
winter 2016 |
0.988 |
0.000e+00 |
0.495 |
0.500 |
2.601 |
NaN |
NaN |
forest |
winter 2016 |
0.994 |
0.000e+00 |
0.479 |
0.477 |
2.402 |
0.456 |
2.711 |
baseline |
winter 2017 |
0.964 |
0.023 |
0.454 |
0.474 |
2.287 |
NaN |
NaN |
forest |
winter 2017 |
0.964 |
0.000e+00 |
0.398 |
0.456 |
1.841 |
0.493 |
2.351 |
baseline |
winter 2019 |
1.000 |
0.059 |
0.284 |
0.364 |
2.164 |
NaN |
NaN |
forest |
winter 2019 |
1.000 |
0.118 |
0.240 |
0.356 |
1.651 |
0.459 |
1.791 |
baseline |
all |
0.984 |
0.023 |
0.428 |
0.457 |
2.601 |
NaN |
NaN |
forest |
all |
0.987 |
0.023 |
0.391 |
0.440 |
2.402 |
0.469 |
2.358 |
Random forest plots
Extended logistic regression results
names |
period |
power |
significance |
meanSquareError |
absError |
maxError |
CRPS |
IGN |
baseline |
winter 2016 |
0.988 |
0.000e+00 |
0.495 |
0.500 |
2.601 |
NaN |
NaN |
elr |
winter 2016 |
1.000 |
0.038 |
0.464 |
0.489 |
2.430 |
0.550 |
3.927 |
baseline |
winter 2017 |
0.964 |
0.023 |
0.454 |
0.474 |
2.287 |
NaN |
NaN |
elr |
winter 2017 |
0.964 |
0.000e+00 |
0.443 |
0.511 |
1.870 |
0.624 |
4.819 |
baseline |
winter 2019 |
1.000 |
0.059 |
0.284 |
0.364 |
2.164 |
NaN |
NaN |
elr |
winter 2019 |
1.000 |
0.118 |
0.222 |
0.354 |
1.403 |
0.488 |
2.301 |
baseline |
all |
0.984 |
0.023 |
0.428 |
0.457 |
2.601 |
NaN |
NaN |
elr |
all |
0.989 |
0.035 |
0.395 |
0.462 |
2.430 |
0.559 |
3.811 |
Extended logistic regression plots