The two known bugs were almost a trap in how easy they felt.
Start by running the test suite to see which tests fail and read the error messages to understand the symptoms. Then systematically debug each failure, starting with the documented bugs, and use a divide-and-conquer approach to isolate the new bugs. Finally, fix each bug and re-run tests to ensure all pass and the model trains and evaluates correctly.
Pro tip: Demonstrate a methodical debugging process: reproduce the failure, form a hypothesis, test it, and fix. Also, consider edge cases like tensor shapes, data types, and device placement, which are common sources of bugs in transformer models.
Execute the test suite to identify which tests fail and capture the full error messages and stack traces. Read the documentation for the two known bugs to understand their expected fixes.
Address the two documented bugs first, as their fixes are likely straightforward and may resolve some test failures. Verify each fix by running the specific failing test.
For remaining failures, use a divide-and-conquer approach: isolate the failing component (e.g., attention, layer norm, loss function) by writing minimal reproduction scripts or adding debug prints. Check tensor shapes, data types, and device consistency.
Apply fixes for the new bugs, ensuring they align with the model's architecture and expected behavior. Re-run all tests to confirm all pass and the model trains and evaluates correctly.
Summarize the root causes and consider adding unit tests or assertions to catch similar issues. Discuss trade-offs if multiple solutions exist.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Start by framing the problem: clarify the dataset, target, and evaluation goals. Then outline a structured pipeline: data loading, EDA (class balance, feature distributions), preprocessing, model training with cross-validation, and evaluation using appropriate metrics. Emphasize the importance of handling class imbalance and selecting metrics aligned with business objectives.
Pro tip: Always discuss the trade-offs between different metrics (e.g., precision vs. recall) and how they relate to the product's goals. Mention that you would set up a baseline model first to establish a performance benchmark before iterating.
Load the dataset, inspect its structure, and clarify the prediction task, target variable, and success criteria. Identify potential issues like missing values or data leakage.
Analyze class balance using value counts and visualize feature distributions with histograms or box plots. Check for correlations and outliers that might affect modeling.
Handle missing values, encode categorical variables, scale numerical features, and split data into training and validation sets (or use cross-validation). Address class imbalance with techniques like SMOTE or class weights if needed.
Train a baseline model (e.g., logistic regression) and a more complex model (e.g., random forest or gradient boosting). Evaluate using metrics like accuracy, precision, recall, F1-score, and AUC-ROC, considering the class balance.
Report key metrics, feature importances, and insights from EDA. Discuss how the model performs relative to business goals and suggest next steps for improvement.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.