Mathematical Problems in Engineering / 2019 / Article / Tab 3

Research Article

Towards Optimization of Boosting Models for Formation Lithology Identification

Table 3

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

PrecisionRecallf1 score

AdaBoost
C0.9960.9350.964
CR0.960.9010.925
CS0.6290.5850.603
FS0.7880.780.783
M0.8590.8720.863
MS0.7790.8350.805
PS0.820.8070.811
S0.7860.7910.777
Avg0.8250.8180.819

XGBoost
C0.9910.9380.963
CR0.9410.9570.947
CS0.5920.6290.601
FS0.7580.7820.767
M0.8550.8620.857
MS0.7880.7590.771
PS0.7930.7960.791
S0.7620.740.74
Avg0.810.8010.802

Gradient Tree Boosting
C0.9690.9610.964
CR0.930.9240.925
CS0.6140.6050.602
FS0.8120.7790.793
M0.8810.8610.869
MS0.7810.8150.793
PS0.840.8320.833
S0.8010.8320.808
Avg0.8260.8240.823

Stacking result
C0.990.9620.974
CR0.940.9490.943
CS0.640.6680.648
FS0.8190.790.802
M0.8880.8740.879
MS0.8010.8090.803
PS0.8390.820.826
S0.7980.8580.819
Avg0.8410.8330.832

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