GMS location: 905
Random forest results
names |
period |
power |
significance |
meanSquareError |
absError |
maxError |
CRPS |
IGN |
baseline |
winter 2016 |
0.979 |
0.000e+00 |
0.317 |
0.417 |
1.772 |
NaN |
NaN |
forest |
winter 2016 |
0.984 |
0.067 |
0.271 |
0.393 |
1.515 |
0.482 |
3.683 |
baseline |
winter 2017 |
0.956 |
0.028 |
0.375 |
0.457 |
2.498 |
NaN |
NaN |
forest |
winter 2017 |
0.933 |
0.083 |
0.308 |
0.412 |
1.885 |
0.473 |
4.258 |
baseline |
winter 2018 |
0.986 |
0.172 |
0.366 |
0.459 |
2.066 |
NaN |
NaN |
forest |
winter 2018 |
0.986 |
0.241 |
0.336 |
0.427 |
1.845 |
0.489 |
3.223 |
baseline |
winter 2019 |
0.986 |
0.000e+00 |
0.283 |
0.384 |
1.969 |
NaN |
NaN |
forest |
winter 2019 |
0.986 |
0.059 |
0.235 |
0.348 |
1.600 |
0.470 |
2.887 |
baseline |
all |
0.979 |
0.062 |
0.332 |
0.427 |
2.498 |
NaN |
NaN |
forest |
all |
0.977 |
0.124 |
0.286 |
0.395 |
1.885 |
0.479 |
3.477 |
Random forest plots
Extended logistic regression results
names |
period |
power |
significance |
meanSquareError |
absError |
maxError |
CRPS |
IGN |
baseline |
winter 2016 |
0.979 |
0.000e+00 |
0.317 |
0.417 |
1.772 |
NaN |
NaN |
elr |
winter 2016 |
0.990 |
0.000e+00 |
0.291 |
0.410 |
1.695 |
0.532 |
3.885 |
baseline |
winter 2017 |
0.956 |
0.028 |
0.375 |
0.457 |
2.498 |
NaN |
NaN |
elr |
winter 2017 |
0.944 |
0.056 |
0.339 |
0.436 |
2.160 |
0.542 |
4.777 |
baseline |
winter 2018 |
0.986 |
0.172 |
0.366 |
0.459 |
2.066 |
NaN |
NaN |
elr |
winter 2018 |
0.986 |
0.172 |
0.351 |
0.449 |
2.274 |
0.516 |
4.214 |
baseline |
winter 2019 |
0.986 |
0.000e+00 |
0.283 |
0.384 |
1.969 |
NaN |
NaN |
elr |
winter 2019 |
0.993 |
0.118 |
0.249 |
0.373 |
1.481 |
0.491 |
3.097 |
baseline |
all |
0.979 |
0.062 |
0.332 |
0.427 |
2.498 |
NaN |
NaN |
elr |
all |
0.983 |
0.093 |
0.305 |
0.416 |
2.274 |
0.520 |
3.947 |
Extended logistic regression plots