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Bytedance·Software Engineer·Technical Phone Screen·Senior

Senior
Apr 2026

Summary

Bytedance Research Scientist interview with a metrics filtering problem that sounds straightforward until you're actually sitting there doing the arithmetic on five different matrices under time pressure.

Questions Asked (1)

Q1

You are given five confusion matrices, each with TP, FP, FN, and TN values. Which matrices satisfy both a recall above 90% and a false positive rate below 10%?

Product Analytics & MetricsAlgorithms & Data Structures
Author's notes

The setup sounds easy and then you realize you have to compute two separate rates for each of five matrices without making arithmetic mistakes while someone is watching you.

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AI HintsAI Generated

Suggested Approach

First, recall the formulas for recall (TP/(TP+FN)) and false positive rate (FP/(FP+TN)). Then, for each matrix, compute these two metrics and check if recall > 0.9 and FPR < 0.1. Clearly state which matrices satisfy both conditions.

Pro tip: Mention that these thresholds are common in fraud detection or medical screening where high recall is critical, but also note the trade-off with precision. This shows you understand the business context behind the metrics.

1. Recall the definitions

Write down the formulas: Recall = TP / (TP + FN) and False Positive Rate = FP / (FP + TN). Ensure you understand that recall is also called sensitivity or true positive rate.

2. Compute metrics for each matrix

For each of the five confusion matrices, calculate recall and FPR. Be meticulous with arithmetic to avoid errors.

3. Apply thresholds

Check if recall > 0.9 (i.e., above 90%) and FPR < 0.1 (i.e., below 10%). Mark matrices that satisfy both conditions.

4. Present results and discuss

List the matrices that meet the criteria. Optionally, discuss the implications of these thresholds and any trade-offs between recall and FPR.

Key Points to Mention

  • Definition of recall and false positive rate
  • Calculation steps for each matrix
  • Thresholds: recall > 90%, FPR < 10%
  • Identification of matrices meeting both conditions
  • Potential trade-off between recall and FPR
  • Business context: high recall often important in fraud detection or medical diagnosis

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