GMS location: 854
Random forest results
names |
period |
power |
significance |
meanSquareError |
absError |
maxError |
CRPS |
IGN |
baseline |
winter 2016 |
0.973 |
0.091 |
0.347 |
0.423 |
2.287 |
NaN |
NaN |
forest |
winter 2016 |
0.973 |
0.182 |
0.338 |
0.419 |
2.156 |
0.578 |
1.635 |
baseline |
winter 2017 |
0.991 |
0.103 |
2.691 |
0.672 |
1.420e+01 |
NaN |
NaN |
forest |
winter 2017 |
0.982 |
0.103 |
2.606 |
0.673 |
1.407e+01 |
0.472 |
2.214 |
baseline |
winter 2018 |
0.993 |
0.121 |
0.504 |
0.477 |
3.993 |
NaN |
NaN |
forest |
winter 2018 |
0.985 |
0.151 |
0.580 |
0.524 |
3.908 |
0.662 |
1.998 |
baseline |
winter 2019 |
1.000 |
0.000e+00 |
0.262 |
0.369 |
1.701 |
NaN |
NaN |
forest |
winter 2019 |
1.000 |
0.000e+00 |
0.225 |
0.361 |
1.330 |
0.598 |
1.657 |
baseline |
all |
0.988 |
0.085 |
0.888 |
0.480 |
1.420e+01 |
NaN |
NaN |
forest |
all |
0.984 |
0.110 |
0.877 |
0.488 |
1.407e+01 |
0.580 |
1.859 |
Random forest plots
Extended logistic regression results
names |
period |
power |
significance |
meanSquareError |
absError |
maxError |
CRPS |
IGN |
baseline |
winter 2016 |
0.973 |
0.091 |
0.347 |
0.423 |
2.287 |
NaN |
NaN |
elr |
winter 2016 |
0.957 |
0.091 |
0.357 |
0.445 |
2.230 |
0.491 |
1.352 |
baseline |
winter 2017 |
0.991 |
0.103 |
2.691 |
0.672 |
1.420e+01 |
NaN |
NaN |
elr |
winter 2017 |
0.991 |
0.077 |
2.615 |
0.702 |
1.398e+01 |
0.510 |
2.802 |
baseline |
winter 2018 |
0.993 |
0.121 |
0.504 |
0.477 |
3.993 |
NaN |
NaN |
elr |
winter 2018 |
0.985 |
0.121 |
0.511 |
0.507 |
3.857 |
0.459 |
1.321 |
baseline |
winter 2019 |
1.000 |
0.000e+00 |
0.262 |
0.369 |
1.701 |
NaN |
NaN |
elr |
winter 2019 |
1.000 |
0.000e+00 |
0.248 |
0.377 |
1.465 |
0.434 |
1.180 |
baseline |
all |
0.988 |
0.085 |
0.888 |
0.480 |
1.420e+01 |
NaN |
NaN |
elr |
all |
0.981 |
0.076 |
0.872 |
0.502 |
1.398e+01 |
0.474 |
1.628 |
Extended logistic regression plots