Page 312 - AI Ver 1.0 Class 10
P. 312
UNIT 7
EVALUATION
Learning Outcomes
• Understanding Evaluation • Importance of Evaluation
• Reasons for Inefficiency of AI Model • Terminologies of Model Evaluation
• Confusion Matrix • Terminologies of Confusion Matrix
• Evaluation Matrix for AI Model
After the designing of an AI model, it is important to evaluate the model to see that the model is designed as per
the need and is giving the desired process.
Understanding Evaluation
Model Evaluation is the last stage of the AI Project development cycle. It is the stage of testing the model where
testing data is given to the system and the output generated is evaluated with the actual result to see the accuracy
of the output and the reliability of the AI Model.
The testing data and the training data should be totally different otherwise the model will always predict the
correct data as it will remember the training data and the AI model will not be evaluated properly for its reliability.
This is also known as overfitting.
Importance of Evaluation
Evaluation is a process that critically examines a program. It 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. Following are the some of the advantages of evaluating
a model:
• 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.
310 Touchpad Artificial Intelligence-X

