Mathematical Problems in Engineering / 2019 / Article / Tab 4

Research Article

Towards Optimization of Boosting Models for Formation Lithology Identification

Table 4

Performance matrix of 5-fold cross validation over the AdaBoost classifier, Gradient Tree Boosting classifier, XGBoost classifier, and stacked classifier in the Hangjinqi gas field.

PrecisionRecallf1 score

AdaBoost
C0.90.6750.741
CS0.8360.810.821
FS0.8270.8230.824
M0.9090.9220.914
MS0.8580.8170.835
PS0.8460.9010.872
S0.8590.7160.772
Avg0.8590.8570.856

XGBoost
C0.8250.7250.759
CS0.8220.7920.806
FS0.7960.8160.802
M0.8990.9190.907
MS0.8530.7610.803
PS0.8390.8860.862
S0.8190.8050.805
Avg0.8460.8450.842

Gradient Tree Boosting
C0.90.70.76
CS0.8320.7930.812
FS0.8190.8260.82
M0.9190.9150.916
MS0.8540.8230.836
PS0.8390.8950.865
S0.8430.7550.787
Avg0.8570.8510.85

Stacking result
C0.90.750.801
CS0.8560.7950.823
FS0.8260.8350.827
M0.9190.9240.919
MS0.8630.8310.847
PS0.8470.9040.874
S0.9010.8180.852
Avg0.8680.8670.864

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