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.

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