From: A comparison of machine learning methods for ozone pollution prediction
Model | rMSE \(\downarrow\) | MAE \(\downarrow\) | MAPE \(\downarrow\) | R-square \(\uparrow\) | J2 \(\downarrow\) | Time (min) |
---|---|---|---|---|---|---|
Linr | 12.677/16.196 | 10.09/14.137 | 0.945/2.713 | 0.332/0.305 | 1.632 | 0.01 |
Linr_l2 | 12.677/16.199 | 10.09/14.141 | 0.945/2.714 | 0.332/0.305 | 1.633 | 0 |
Lasso | 13.642/19.561 | 10.91/17.674 | 1.114/3.589 | 0.227/0.267 | 2.056 | 0 |
PLSR | 12.777/17.336 | 10.243/15.319 | 0.934/2.901 | 0.322/0.331 | 1.841 | 0.01 |
GRP_Expo | 9.117/12.612 | 6.847/10.237 | 0.543/1.941 | 0.655/0.387 | 1.914 | 9.64 |
GRP_DotProd | 12.706/16.754 | 10.121/14.705 | 0.963/2.847 | 0.329/0.306 | 1.739 | 121.44 |
GRP_Matern | 0.0/20.868 | 0.0/17.887 | 0.0/1.0 | 1.0/0.0 | inf | 25.47 |
SVR_linear | 12.795/15.982 | 9.99/13.821 | 0.997/2.69 | 0.323/0.218 | 1.56 | 0.01 |
SVR_poly | 11.858/13.271 | 9.358/10.827 | 0.873/2.002 | 0.416/0.177 | 1.253 | 5.45 |
SVR_rbf | 10.66/13.062 | 8.202/10.981 | 0.685/2.007 | 0.528/0.428 | 1.501 | 8.57 |
SVR_sigmoid | 12.764/16.48 | 10.124/14.4 | 0.973/2.778 | 0.323/0.278 | 1.667 | 9.77 |
MLP_1 | 12.681/16.561 | 10.111/14.523 | 0.939/2.755 | 0.332/0.316 | 1.706 | 0.6 |
MLP_2 | 15.514/21.591 | 12.682/19.384 | 1.283/4.199 | 0.0/0.0 | 1.937 | 1.35 |
RF | 10.116/13.874 | 7.801/11.75 | 0.657/2.188 | 0.575/0.42 | 1.881 | 0.06 |
Bagging | 3.722/13.254 | 2.497/10.781 | 0.174/2.121 | 0.942/0.363 | 12.681 | 0.18 |
GBoost | 10.272/14.359 | 7.948/12.269 | 0.66/2.306 | 0.562/0.398 | 1.954 | 0.28 |
AdaBoost | 11.704/17.821 | 9.465/16.113 | 0.852/3.047 | 0.431/0.449 | 2.318 | 0.1 |
HistGBoost | 9.036/13.009 | 6.899/10.831 | 0.538/2.147 | 0.661/0.431 | 2.073 | 13.07 |
LightGBM | 11.532/16.499 | 9.204/14.699 | 0.892/3.051 | 0.447/0.422 | 2.047 | 0.01 |