MLS-C01 Modeling Practice Question
A data scientist is tuning a random forest model using SageMaker Hyperparameter Tuning. The objective metric is validation:accuracy. Which THREE hyperparameters are most commonly tuned for random forest? (Choose THREE.)
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
✓
Minimum samples per leaf (min_samples_leaf)
Options B, C, and D are correct. Common tunable hyperparameters for random forest include number of trees (n_estimators), maximum depth (max_depth), and minimum samples per leaf (min_samples_leaf). Option A (learning rate) is for gradient boosting. Option E (batch size) is for neural networks, not random forest.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Learning rate
Why it's wrong here
Learning rate is not a hyperparameter for random forest.
- ✓
Minimum samples per leaf (min_samples_leaf)
Why this is correct
This parameter helps prevent overfitting.
- ✓
Maximum depth (max_depth)
Why this is correct
Depth controls tree complexity.
- ✓
Number of trees (n_estimators)
Why this is correct
Number of trees affects model capacity and overfitting.
- ✗
Batch size
Why it's wrong here
Batch size is irrelevant for random forest.
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