Bingung Pakai Precision atau Recall? Mulai dari Satu Pertanyaan Ini

📰 Medium · Machine Learning

Learn to choose between precision and recall in machine learning by evaluating the costs of false positives and false negatives

intermediate Published 21 Apr 2026
Action Steps
  1. Evaluate the cost of false positives (FP) in your model
  2. Evaluate the cost of false negatives (FN) in your model
  3. Compare the costs of FP and FN to determine which is more critical
  4. Choose to prioritize precision if FP is more costly
  5. Choose to prioritize recall if FN is more costly
Who Needs to Know This

Data scientists and machine learning engineers can benefit from understanding the trade-offs between precision and recall to make informed decisions about their models

Key Insight

💡 The choice between precision and recall depends on the relative costs of false positives and false negatives

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💡 Precision vs Recall: which one to prioritize? Evaluate the costs of false positives and false negatives to make an informed decision
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