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• Evaluation ensures 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.
Model Evaluation Terminologies
Evaluation of an AI model can be done using various terminologies. Let us try to understand them with the help
of a scenario.
Scenario: An AI-based prediction model is deployed in schools. The model is supposed to predict whether the
students of grade 10 will be able to get a “Perfect Score“ in the board exams this coming year or not. The model
will check for whether the students will be able to score full marks in board exams in the coming year or not.
There are two important parameters that are used for the Evaluation of a model. These are:
Prediction: It is the output given by the AI model using a Machine Learning algorithm.
Reality: It is the real scenario of the situation for which the prediction has been made.
Let’s look at the various combinations that can be considered for the above scenario.
Case 1: Is There a Perfect Score?
Due to coaching institutes and a lot of easy access to online
resources, it is not clear whether this would work in their
favour or not. Guidance and coaching are so readily available
to all that most of them are prepared well for the exams, but
it can also lead to tougher papers to test them well. This
makes getting a perfect score unpredictable. We need an
AI model that can predict whether the student can get a
Perfect Score or not in the board exams so that the students Predicion: Yes Reality: Yes
can timely plan their preparation and schedule to study as True Positive
per the exam schedule.
In the above picture, we show the possibility of students scoring full marks in board exams for grade 10. The model
predicts a Yes, which means the student will get a Perfect Score in board exams to be conducted. The prediction
matches with the reality: Yes, therefore, this condition is called True Positive.
Case 2: Is There a Perfect Score?
There is no Perfect Score as the Examination Paper was
difficult, hence the Reality is No. In this case, the machine too
has predicted it correctly as a No. Therefore, this condition is
termed as True Negative.
Predicion: No Reality: No
True Negative
Case 3: Is There a Perfect Score?
Here, the reality is that there are no Perfect Scores due
to difficult papers. However, the machine has incorrectly
predicted that there will be a Perfect Score for the students Predicion: Yes Reality: No
of grade 10. This case is termed as False Positive. False Positive
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