AI0-001 AI Concepts and Techniques Practice Question
A machine learning team is splitting a dataset for a binary classification problem. They want to ensure robust evaluation and avoid data leakage. Which TWO practices should they follow? (Choose 2)
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
✓
Split into training, validation, and test sets
Train/validation/test split is standard; cross-validation gives more robust estimates. Shuffling before split prevents ordering bias.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Normalise the entire dataset before splitting
Why it's wrong here
Normalisation should be fit on training set only and applied to others; doing it before split leaks information.
- ✓
Split into training, validation, and test sets
Why this is correct
A three-way split allows tuning on validation and final evaluation on test.
- ✗
Include validation data in the training set for more data
Why it's wrong here
Mixing validation with training causes data leakage and biased evaluation.
- ✓
Shuffle the data before splitting
Why this is correct
Shuffling ensures random distribution, preventing bias from data order.
- ✗
Use the same split for all experiments
Why it's wrong here
Using the same split can lead to overfitting to that specific split; cross-validation is better.
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