AI Associate AI Fundamentals Practice Question
Exhibit
Custom Model Configuration: - Name: LeadScorer_v2 - Algorithm: XGBoost - Training Data: 100,000 records (80% won, 20% lost) - Evaluation Metric: Accuracy
Refer to the exhibit. A data scientist built a model using training data where 80% of leads were won. The model achieved 80% accuracy. What is the main issue with this evaluation?
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
✓
Accuracy is not a reliable metric because the data is imbalanced
Correct: Accuracy is misleading due to class imbalance; a model that always predicts 'won' would get 80% accuracy. Option A: Data size is fine. Option B: XGBoost is good for tabular data. Option D: Confidence score not provided.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
The model lacks confidence scoring
Why it's wrong here
While confidence is important, the immediate issue is the misleading accuracy metric.
- ✗
The algorithm choice (XGBoost) is inappropriate
Why it's wrong here
XGBoost is commonly used for classification tasks like lead scoring.
- ✓
Accuracy is not a reliable metric because the data is imbalanced
Why this is correct
With 80% won leads, a constant 'won' prediction yields 80% accuracy, so accuracy does not measure model's discriminative power.
- ✗
The training data size is insufficient
Why it's wrong here
100,000 records is generally sufficient for a lead scoring model.
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