Evaluation

The process of testing the project/machine like its performance or capabilities and finding faults etc. is known as Evaluation.

Moving towards deploying the model in the real world, there is a need to test it in as many ways as possible. Therefore, the stage of testing the models is known as EVALUATION.

OR

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.

OR

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.

Why is evaluation important?

• Evaluation is important to ensure that the model is operating correctly and optimally.
• Evaluation is an initiative to understand how well it achieves its goals.
• Evaluations help to determine what works well and what could be improved in a program

Overfitting of Data

Models that use the training dataset during testing, will always results in correct output. This is known as overfitting.
To evaluate the AI model, it is not necessary to use the data that is used to build the model because AI Model remembers the whole training data set, therefore it always predicts the correct label for any point in the training dataset. This is known as Overfitting.