CYBERCRIME — CONFUSION MATRIX

What is a Confusion Matrix?

Confusion Matrix
  • Positive(P): The predicted result is Positive (Example: Image is a cat)
  • Negative(N): the predicted result is Negative (Example: Images is not a cat)
  1. True Positive: This column holds the number of data out of the total, which is True in actual data and is correctly predicted by the machine.
  2. False Positive: This column hold the number of data out of the total, which is True in actual data, but the machine predicted them false. (Type 1 Error )
  3. False Negative: This column holds the number of data out of the total, which is False in actual data and machine predicted then wrong, i.e., True.(Type 2 Error)
  4. True Negative: This column holds the number of data out of the total, which is False in actual data, and the machine also predicted then false, i.e., which means correct prediction.

Type I error:

Type II error:

CONCLUSION :

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