Research Article
The Prediction of Sinter Drums Strength Using Hybrid Machine Learning Algorithms
Table 3
The prediction accuracy of the sinter drum strength.
| | Scaler | Min_max | Max_abs | Robust | Quantile | Function |
| Linear | 0.189 | 0.158 | 0.48 | −0.081 | 0.227 | 0.368 | Ridge | 0.192 | 0.137 | 0.227 | −0.075 | 0.238 | 0.379 | Tree | −1.408 | 0.178 | −0.065 | −0.465 | -0.68 | 0.252 | SVR | 0.112 | 0.146 | −1.586 | 0.108 | 0.119 | −0.184 | Kneighbors | 0.182 | 0.398 | 0.301 | −0.14 | 0.311 | 0.38 | RandomForest | 0.045 | 0.306 | 0.239 | 0.016 | 0.332 | 0.565 | AdaBoost | 0.12 | 0.141 | 0.279 | −0.077 | 0.085 | 0.439 | G-boosting | −0.031 | 0.282 | 0.404 | −0.126 | 0.158 | 0.644 | Bagging | 0.089 | 0.301 | 0.316 | −0.149 | 0.039 | 0.475 | ExtraTree | −1.165 | 0.242 | −0.448 | −0.407 | −0.487 | 0.532 |
|
|