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5. Evaluation

After successfully selecting and implementing the model, the project is complete and perform the task that are designed for. Now, it's time to evaluate the performance of the project, finding out the possible faults or errors to check whether it meet the desired goal or not.

Evaluation is a process of understanding the reliability of any AI model, based on outputs by feeding the test dataset into the model and comparing it with actual answers.
Evaluation involves collecting and analyzing information about a program’s activities, characteristics, and outcomes. Its purpose is to make judgments about a program, to improve its effectiveness, and/or to inform programming decisions.

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.

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.