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CodeSignal

Choosing Inputs and Reading Results Responsibly

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: Choosing Defensible Inputs
    • Understanding Inputs in Everyday Work
    • Screening a Proposed Input List
    • Trimming the Input List
  • Unit 2: Spotting Leakage and Proxies
    • Spotting Hindsight Data in Predictions
    • Sorting a Field List: Usable, Hindsight, Proxy
    • Catching the Too Good Number
  • Unit 3: Reading Confidence Scores
    • What Confidence Scores Really Mean
    • Judging Confidence Scores in Refunds
    • Briefing a Team on Confidence Bands
  • Unit 4: Questioning Correlation Before Policy
    • Sorting Claims About a Finding
    • Quick Check: Pattern, Cause, and Next Evidence
    • Defending the Claim Before the Board

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