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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?
Predicion: Yes Reality: Yes
True Positive
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 can timely plan their preparation and schedule to study as 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?
Predicion: No Reality: No
True Negative
AI Reflection, Project Cycle and Ethics 213

