GMS location: 830
Random forest results
names |
period |
power |
significance |
meanSquareError |
absError |
maxError |
CRPS |
IGN |
baseline |
winter 2016 |
0.982 |
0.048 |
0.375 |
0.461 |
2.328 |
NaN |
NaN |
forest |
winter 2016 |
0.982 |
0.048 |
0.326 |
0.411 |
2.238 |
0.507 |
5.250 |
baseline |
winter 2017 |
0.982 |
0.023 |
0.389 |
0.433 |
2.567 |
NaN |
NaN |
forest |
winter 2017 |
0.982 |
0.023 |
0.302 |
0.384 |
1.942 |
0.470 |
4.044 |
baseline |
winter 2018 |
0.986 |
0.056 |
0.238 |
0.360 |
2.100 |
NaN |
NaN |
forest |
winter 2018 |
0.993 |
0.056 |
0.226 |
0.355 |
2.202 |
0.481 |
2.885 |
baseline |
winter 2019 |
1.000 |
0.143 |
0.279 |
0.372 |
2.007 |
NaN |
NaN |
forest |
winter 2019 |
1.000 |
0.095 |
0.220 |
0.355 |
1.902 |
0.488 |
3.661 |
baseline |
all |
0.988 |
0.058 |
0.319 |
0.407 |
2.567 |
NaN |
NaN |
forest |
all |
0.990 |
0.050 |
0.268 |
0.377 |
2.238 |
0.487 |
3.973 |
Random forest plots
Extended logistic regression results
names |
period |
power |
significance |
meanSquareError |
absError |
maxError |
CRPS |
IGN |
baseline |
winter 2016 |
0.982 |
0.048 |
0.375 |
0.461 |
2.328 |
NaN |
NaN |
elr |
winter 2016 |
0.988 |
0.000e+00 |
0.369 |
0.455 |
2.120 |
0.605 |
6.634 |
baseline |
winter 2017 |
0.982 |
0.023 |
0.389 |
0.433 |
2.567 |
NaN |
NaN |
elr |
winter 2017 |
0.991 |
0.023 |
0.335 |
0.408 |
2.267 |
0.512 |
4.944 |
baseline |
winter 2018 |
0.986 |
0.056 |
0.238 |
0.360 |
2.100 |
NaN |
NaN |
elr |
winter 2018 |
0.993 |
0.056 |
0.251 |
0.385 |
2.027 |
0.562 |
4.743 |
baseline |
winter 2019 |
1.000 |
0.143 |
0.279 |
0.372 |
2.007 |
NaN |
NaN |
elr |
winter 2019 |
1.000 |
0.143 |
0.254 |
0.393 |
1.848 |
0.525 |
4.076 |
baseline |
all |
0.988 |
0.058 |
0.319 |
0.407 |
2.567 |
NaN |
NaN |
elr |
all |
0.993 |
0.050 |
0.302 |
0.411 |
2.267 |
0.554 |
5.142 |
Extended logistic regression plots