GMS location: 507
Random forest results
names |
period |
power |
significance |
meanSquareError |
absError |
maxError |
CRPS |
IGN |
baseline |
winter 2016 |
0.989 |
0.000e+00 |
0.327 |
0.444 |
1.700 |
NaN |
NaN |
forest |
winter 2016 |
0.995 |
0.059 |
0.274 |
0.394 |
1.573 |
0.439 |
1.424 |
baseline |
winter 2017 |
0.984 |
0.000e+00 |
0.581 |
0.515 |
4.858 |
NaN |
NaN |
forest |
winter 2017 |
0.968 |
0.000e+00 |
0.457 |
0.446 |
4.478 |
0.445 |
1.598 |
baseline |
winter 2018 |
0.980 |
0.100 |
0.470 |
0.475 |
2.957 |
NaN |
NaN |
forest |
winter 2018 |
0.974 |
0.067 |
0.386 |
0.413 |
2.697 |
0.443 |
1.490 |
baseline |
winter 2019 |
0.993 |
0.077 |
0.627 |
0.469 |
4.203 |
NaN |
NaN |
forest |
winter 2019 |
0.993 |
0.077 |
0.548 |
0.437 |
4.104 |
0.435 |
1.693 |
baseline |
all |
0.987 |
0.044 |
0.488 |
0.473 |
4.858 |
NaN |
NaN |
forest |
all |
0.984 |
0.044 |
0.406 |
0.420 |
4.478 |
0.440 |
1.541 |
Random forest plots
Extended logistic regression results
names |
period |
power |
significance |
meanSquareError |
absError |
maxError |
CRPS |
IGN |
baseline |
winter 2016 |
0.989 |
0.000e+00 |
0.327 |
0.444 |
1.700 |
NaN |
NaN |
elr |
winter 2016 |
0.995 |
0.000e+00 |
0.280 |
0.416 |
1.505 |
0.496 |
1.980 |
baseline |
winter 2017 |
0.984 |
0.000e+00 |
0.581 |
0.515 |
4.858 |
NaN |
NaN |
elr |
winter 2017 |
0.976 |
0.000e+00 |
0.545 |
0.489 |
4.824 |
0.502 |
2.486 |
baseline |
winter 2018 |
0.980 |
0.100 |
0.470 |
0.475 |
2.957 |
NaN |
NaN |
elr |
winter 2018 |
0.993 |
0.133 |
0.435 |
0.454 |
2.781 |
0.480 |
2.107 |
baseline |
winter 2019 |
0.993 |
0.077 |
0.627 |
0.469 |
4.203 |
NaN |
NaN |
elr |
winter 2019 |
0.993 |
0.154 |
0.595 |
0.471 |
4.232 |
0.505 |
2.952 |
baseline |
all |
0.987 |
0.044 |
0.488 |
0.473 |
4.858 |
NaN |
NaN |
elr |
all |
0.990 |
0.067 |
0.450 |
0.454 |
4.824 |
0.495 |
2.347 |
Extended logistic regression plots