MLS-C01 Modeling Practice Question
A machine learning team is using SageMaker to train a model with the built-in Linear Learner algorithm. The dataset has 1 million rows and 20 features. The training completes, but the model's mean squared error (MSE) is high. Which parameter adjustment is most likely to reduce MSE?
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 number of epochs
Increasing the number of epochs allows the model to see the data more times, helping it converge to a lower training error, thus reducing MSE. Option A is incorrect: increasing mini-batch size typically improves computational efficiency but can make convergence slower per epoch, potentially requiring even more epochs to converge; it may not directly reduce MSE. Option B is incorrect: cross-entropy is a loss function for classification problems, not regression; Linear Learner with MSE is appropriate for regression. Option D is incorrect: increasing the learning rate can cause the optimizer to overshoot the minimum or diverge, often increasing rather than decreasing MSE.
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 the mini-batch size
Why it's wrong here
Increasing mini-batch size typically improves computational efficiency but can make convergence slower per epoch, potentially requiring even more epochs to converge; it may not directly reduce MSE.
- ✗
Change the loss function to cross-entropy
Why it's wrong here
Cross-entropy is a loss function for classification problems, not regression; Linear Learner with MSE is appropriate for regression.
- ✓
Increase the number of epochs
Why this is correct
Increasing the number of epochs allows the model to see the data more times, helping it converge to a lower training error, thus reducing MSE.
- ✗
Increase the learning rate
Why it's wrong here
Increasing the learning rate can cause the optimizer to overshoot the minimum or diverge, often increasing rather than decreasing MSE.
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