PDE Preparing and Using Data for Analysis Practice Question
You are using BigQuery ML to train a matrix factorization model for a recommendation system. The training data consists of user-item interactions. You notice that the model is overfitting. Which of the following hyperparameter changes would most likely 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 w_reg (regularization weight) from 0.1 to 0.5
Increasing the L2 regularization weight (w_reg) penalizes large weights and reduces overfitting. Increasing number of factors (num_factors) increases model complexity, worsening overfitting. Decreasing learning rate may help but not as directly as regularization.
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 w_reg (regularization weight) from 0.1 to 0.5
Why this is correct
Increasing regularization penalizes large weights and reduces overfitting.
- ✗
Decrease w_reg (regularization weight) from 0.1 to 0.01
Why it's wrong here
Decreasing regularization reduces penalty, likely increasing overfitting.
- ✗
Increase num_factors from 10 to 20
Why it's wrong here
Increasing factors increases model complexity, likely worsening overfitting.
- ✗
Increase num_training_iterations from 10 to 20
Why it's wrong here
More iterations may lead to overfitting if not regularized properly.
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