MLS-C01 Modeling Practice Question
A data scientist is training a gradient boosting model using SageMaker's built-in XGBoost algorithm. The model is overfitting on the training data. Which hyperparameter adjustment is most likely to reduce overfitting?
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 lambda (L2 regularization)
Increasing the L2 regularization term (lambda) penalizes large weights, which helps reduce overfitting. Option A is incorrect because increasing the learning rate (eta) can cause the model to converge too quickly and may lead to overfitting if not paired with proper regularization. Option B is incorrect because increasing max_depth increases model complexity, which typically worsens overfitting. Option C is incorrect because increasing num_round (number of boosting rounds) allows the model to fit the training data more closely, increasing the risk of overfitting.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Increase learning rate (eta)
Why it's wrong here
Higher learning rate can cause overfitting if not regularized.
- ✗
Increase max_depth
Why it's wrong here
Increasing max_depth makes trees deeper, increasing overfitting risk.
- ✗
Increase num_round
Why it's wrong here
More boosting rounds can lead to overfitting.
- ✓
Increase lambda (L2 regularization)
Why this is correct
Higher lambda penalizes large weights, reducing overfitting.
Quick reference
Cloud Service Model Comparison
| Model | You Manage | Provider Manages | Examples |
|---|---|---|---|
| IaaS | OS, runtime, apps, data | Hardware, hypervisor, networking | EC2, Azure VMs, GCP Compute Engine |
| PaaS | Apps and data | OS, runtime, middleware, hardware | Elastic Beanstalk, Azure App Service |
| SaaS | Data and settings only | Everything else | Microsoft 365, Salesforce, Workday |
| FaaS / Serverless | Function code only | Infra, scaling, runtime | Lambda, Azure Functions, Cloud Run |
| CaaS | Containers and apps | Kubernetes, OS, hardware | EKS, AKS, GKE |
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