MLS-C01 · Question #117
A Machine Learning Specialist is assigned to a Fraud Detection team and must tune an XGBoost model, which is working appropriately for test data. However, with unknown data, it is not working as…
The correct answer is B. Lower the max_depth parameter value. Lower min_child_weight, the tree becomes more deep and complex. Increase min_child_weight, the tree will have less branches and less complexity.
Question
A Machine Learning Specialist is assigned to a Fraud Detection team and must tune an XGBoost model, which is working appropriately for test data. However, with unknown data, it is not working as expected. The existing parameters are provided as follows. Which parameter tuning guidelines should the Specialist follow to avoid overfitting?
Exhibit
Options
- AIncrease the max_depth parameter value.
- BLower the max_depth parameter value.
- CUpdate the objective to binary:logistic.
- DLower the min_child_weight parameter value.
How the community answered
(48 responses)- A4% (2)
- B83% (40)
- C2% (1)
- D10% (5)
Explanation
Lower min_child_weight, the tree becomes more deep and complex. Increase min_child_weight, the tree will have less branches and less complexity.
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