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CodeSignal

From Training Data to Practical Evaluation

via CodeSignal

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Overview

Learn to distinguish between predicting recorded outcomes and grouping unfamiliar patterns. You’ll practice assessing if data reflects your real-world cases and treating evaluation as a business decision. Master the "Fresh-Case Check" and the "Two-Mistake Trade-off" to weigh missed cases against false alarms, ensuring AI performance aligns with your operational tolerance for risk.

Syllabus

  • Unit 1: Understanding Your Data Limits
    • Classification or Grouping?
    • Reading a CRM Inventory Record by Record
    • Reframing a Promise Already Made
  • Unit 2: Assessing Training Data Coverage
    • Explaining a System's Coverage Boundary
    • Spotting Hidden Gaps in Hiring Data
    • Narrowing Scope Before the Rollout
  • Unit 3: Evaluating Unseen Case Performance
    • Matching Evidence to What It Proves
    • Choosing Pilots That Prove Real Performance
    • Locking In a Credible Pilot Sample
  • Unit 4: Balancing Misses and False Alarms
    • Reading Screening Outcomes for Review Teams
    • Weighing Missed Cases Against False Alarms
    • Setting the Alert Threshold

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