PDE Preparing and Using Data for Analysis Practice Question
A data scientist is using AutoML Tables to build a classification model for predicting customer churn. The dataset is highly imbalanced (only 1% churn). Which strategy should they use to handle the class imbalance within AutoML Tables?
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
✓
No action needed; AutoML Tables automatically handles class imbalance by adjusting class weights.
AutoML Tables automatically computes class weights and applies them during training to handle imbalanced data. You do not need to manually apply SMOTE or change the training budget; AutoML Tables handles it out of the box.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Manually apply SMOTE to the training data before uploading to AutoML Tables.
Why it's wrong here
AutoML Tables does not support SMOTE; it uses class weights internally.
- ✓
No action needed; AutoML Tables automatically handles class imbalance by adjusting class weights.
Why this is correct
Correct.
- ✗
Enable the 'enable_class_imbalance_handling' flag during training.
Why it's wrong here
No such flag exists; AutoML Tables handles imbalance automatically.
- ✗
Set the 'class_weight' parameter in the AutoML Tables training configuration to 'balanced'.
Why it's wrong here
AutoML Tables automatically weights classes; no such parameter exists.
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