Page 313 - AI Ver 1.0 Class 10
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Reasons for Inefficiency of AI Model
Sometimes AI Model is not very efficient due to the following reasons:
• Lack of Training Data: The lack of training data could be due to less data available for developing an AI model,
or the data is missed while training the model then the AI model created will not be efficient.
• Unauthenticated Data / Wrong Data: If the data is not authenticated and correct due to negligence or data
collected from unauthorized resources then the model will not give good results.
• Inefficient coding / Wrong Algorithms: If the written algorithms are not correct and relevant, AI model will
not give desired output.
• Not Tested: If the model is not tested properly, then it will not be efficient.
• Not Easy: If it is not easy to be implemented in production or scalable.
• Less Accuracy: A model is not efficient if it gives less accuracy scores in production or test data or if it is not able
to generalize well on unseen data.
Terminologies of Model Evaluation
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 12 will be taking board exams in the coming year or not. The model will be checking for whether there will
be board exams in the coming year or not.
There are two important parameters which are used for the Evaluation of a model. These are:
• Prediction: It is the output given by the AI model using 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 Board Exam?
Predicion:
Yes Reality: Yes
True Positive
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