GMS location: 503
Random forest results
names |
period |
power |
significance |
meanSquareError |
absError |
maxError |
CRPS |
IGN |
baseline |
winter 2016 |
0.994 |
0.242 |
0.459 |
0.500 |
2.960 |
NaN |
NaN |
forest |
winter 2016 |
0.994 |
0.242 |
0.454 |
0.517 |
2.661 |
0.581 |
2.395 |
baseline |
winter 2017 |
0.974 |
0.079 |
0.496 |
0.499 |
2.801 |
NaN |
NaN |
forest |
winter 2017 |
0.957 |
0.079 |
0.466 |
0.486 |
2.877 |
0.536 |
1.957 |
baseline |
winter 2018 |
0.991 |
0.250 |
0.430 |
0.490 |
1.998 |
NaN |
NaN |
forest |
winter 2018 |
0.991 |
0.062 |
0.333 |
0.418 |
2.342 |
0.581 |
2.002 |
baseline |
winter 2019 |
0.985 |
0.316 |
0.633 |
0.574 |
2.892 |
NaN |
NaN |
forest |
winter 2019 |
0.978 |
0.158 |
0.559 |
0.495 |
3.274 |
0.567 |
2.372 |
baseline |
all |
0.987 |
0.205 |
0.503 |
0.515 |
2.960 |
NaN |
NaN |
forest |
all |
0.981 |
0.131 |
0.455 |
0.483 |
3.274 |
0.567 |
2.201 |
Random forest plots
Extended logistic regression results
names |
period |
power |
significance |
meanSquareError |
absError |
maxError |
CRPS |
IGN |
baseline |
winter 2016 |
0.994 |
0.242 |
0.459 |
0.500 |
2.960 |
NaN |
NaN |
elr |
winter 2016 |
0.994 |
0.151 |
0.553 |
0.589 |
2.543 |
0.727 |
4.781 |
baseline |
winter 2017 |
0.974 |
0.079 |
0.496 |
0.499 |
2.801 |
NaN |
NaN |
elr |
winter 2017 |
0.965 |
0.079 |
0.481 |
0.497 |
3.147 |
0.580 |
2.982 |
baseline |
winter 2018 |
0.991 |
0.250 |
0.430 |
0.490 |
1.998 |
NaN |
NaN |
elr |
winter 2018 |
0.991 |
0.125 |
0.366 |
0.449 |
2.061 |
0.616 |
2.837 |
baseline |
winter 2019 |
0.985 |
0.316 |
0.633 |
0.574 |
2.892 |
NaN |
NaN |
elr |
winter 2019 |
0.978 |
0.053 |
0.574 |
0.513 |
3.411 |
0.656 |
3.560 |
baseline |
all |
0.987 |
0.205 |
0.503 |
0.515 |
2.960 |
NaN |
NaN |
elr |
all |
0.983 |
0.107 |
0.500 |
0.519 |
3.411 |
0.651 |
3.644 |
Extended logistic regression plots