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Parameters for Evaluation

Prediction and Reality are the two parameters considered for Evaluation of a model. The “Prediction” is the output which is given by the machine and the “Reality”is the real scenario on which the prediction has been made.

Four cases need to be remember

True Positive(TP)

  • The predicted value matches the actual value.
  • The actual value was positive and the model predicted a positive value.

True Negative(TN)

  • The predicted value matches the actual value.
  • The actual value was negative and the model predicted a negative value.

False Positive (FP)

  • The predicted value was falsely predicted.
  • The actual value was negative but the model predicted a positive value.
  • Also known as the Type 1 error.

False Negative(FN)

  • The predicted value was falsely predicted.
  • The actual value was positive but the model predicted a negative value.
  • Also known as the Type 2 error.