Intuit·Data Scientist·Technical Phone Screen·Intermediate

Intermediate

Jun 2026

Summary

Intuit data scientist interview with a meaty ML evaluation question. The whole thing was one technical problem but it had enough layers to keep you busy for a while.

Questions Asked (1)

Given a dataset of binary classification outputs with actual labels, predicted labels, and a confidence score per record, write code to compute TP, FP, TN, and FN, then calculate precision, recall, and F1. Also explain how you'd derive predicted labels from confidence scores using a threshold, how that affects the metrics, and how you'd handle division-by-zero edge cases.

Product Analytics & MetricsAlgorithms & Data StructuresTechnical Trade-offs

Author's notes

This looked manageable at first and then the threshold part tripped me up a bit.

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AI HintsAI Generated

Suggested Approach

Start by clarifying the data structure and assumptions, then walk through the code to compute the confusion matrix and metrics, explaining each step. Next, discuss threshold selection and its impact on precision/recall, and finally address edge cases like division by zero with practical solutions.

Pro tip: Mention that threshold tuning should be driven by business objectives (e.g., minimizing false negatives in fraud detection) and that you can use precision-recall curves to select an optimal threshold. Also, note that using libraries like scikit-learn can simplify the implementation but be prepared to write the logic from scratch.

1. Clarify data and assumptions

Confirm the dataset schema: actual labels (0/1), predicted labels (0/1), and confidence scores (0-1). Assume binary classification with 1 as positive class.

2. Compute confusion matrix

Write code to iterate through records and count TP, FP, TN, FN based on actual and predicted labels. Alternatively, use vectorized operations for efficiency.

3. Calculate metrics

Compute precision = TP / (TP + FP), recall = TP / (TP + FN), and F1 = 2 * (precision * recall) / (precision + recall). Handle division by zero by returning 0 or using a safe division function.

4. Threshold derivation and impact

Explain that predicted labels can be derived by applying a threshold to confidence scores (e.g., predicted = 1 if score >= threshold). Discuss how varying the threshold trades off precision and recall, and how to choose a threshold based on business needs.

5. Edge cases and robustness

Address division-by-zero scenarios (e.g., no positive predictions) by defining metrics as 0 or using smoothing. Also mention handling empty datasets or all-negative cases.

Key Points to Mention

Confusion matrix definitions: TP, FP, TN, FN.
Precision, recall, and F1 formulas and their interpretations.
Threshold selection: default 0.5, but can be tuned using ROC/PR curves.
Impact of threshold on metrics: increasing threshold reduces FP but may increase FN, affecting precision and recall inversely.
Division-by-zero handling: use conditional checks or safe division (e.g., return 0 when denominator is 0).
Vectorized implementation using numpy/pandas for efficiency, or using scikit-learn's confusion_matrix and classification_report.

AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.