Mathematical Problems in Engineering / 2019 / Article / Tab 2

Research Article

Towards Optimization of Boosting Models for Formation Lithology Identification

Table 2

Tuned parameters for boosting models with search range and optimum value.

Boosting modelTuned parametersSearch rangeOptimum value

AdaBoostLearning rate0.1–0.90.4
Number of iterations50–300200

GTBLearning rate1e − 5–10.3
The minimum number of samples obliged at a leaf node5–2020
Maximum depth of the individual tree5–2020
The number of boosting steps100–200200
The minimum number of samples obliged to split an internal node10–5025
Subsample0.6–10.7

XGBoostLearning rate0–0.30.3
Minimum loss reduction to split0.1–0.50.2
L1 regularization term on terms1e − 5–1e−21e − 4
The minimum number of samples obliged at a leaf node5–5020
Maximum depth of individual tree1–96
Number of boosting steps300–900900
Ratio of columns when constructing trees50–10060
Subsample0.4–10.7
Minimum sum of instance weight1–102

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