MLS-C01 Modeling Practice Question
Network Topology
A data scientist ran a hyperparameter tuning job for an XGBoost model. The tuning job completed, but the best validation RMSE is 2.34. The data scientist believes the model can perform better. Based on the exhibit, which change to the tuning strategy is most likely to improve the model's performance?
⚠ Common exam trap
Watch out — candidates often think increasing `eta` to 1.0 accelerates learning, but they overlook that a high learning rate without sufficient boosting rounds or regularization often causes the model to overshoot the optimal solution, degrading RMSE.
Answer choices
Why each option matters
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
Increase the static num_round hyperparameter to 500
Increasing the static `num_round` hyperparameter to 500 allows the model to train for more boosting rounds, which can reduce underfitting and lower the RMSE further. The current best validation RMSE of 2.34 suggests the model may not have converged, and additional rounds can help the XGBoost model learn more complex patterns, provided overfitting is monitored with early stopping.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Use random search instead of Bayesian optimization
Why it's wrong here
Random search may explore more but not necessarily find better parameters; the limitation is likely the fixed num_round.
- ✗
Change the objective to binary:logistic
Why it's wrong here
The objective is reg:linear for regression; changing to binary classification would be inappropriate for a regression problem.
- ✗
Increase the maximum value of eta to 1.0
Why it's wrong here
A higher eta can cause overshooting; the current range 0.01-0.5 is typical; increasing max may not help.
- ✓
Increase the static num_round hyperparameter to 500
Why this is correct
The tuning job fixed num_round to 100; increasing it allows more boosting rounds, which can improve model performance.
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