Page 321 - AI Ver 1.0 Class 10
P. 321
Video Session
Watch this video,
Visit https://www.youtube.com/watch?v=2osIZ-dSPGE or scan the QR code and answer
the given question.
What did you learn about model evaluation from the video?
At a Glance
• 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.
• Confusion matrix is a tabular structure which helps in measuring the performance of an AI model using the test data.
• Accuracy is the percentage of correct prediction out of the total observations made in an AI model.
• Precision is the percentage of True Positive cases and All Predicted Positive Cases.
• Recall is defined as the fraction of positive cases that are correctly identified. It majorly takes into account the true
reality cases i.e.; it is a measure of our model correctly identifying True Positives.
• F1 score also called F-score or F-measure is the measure of a test’s accuracy. It can be defined as the measure of
balance between precision and recall.
Exercise
Solved Questions
SECTION A (Objective Type Questions)
uiz
A. Tick ( ) the correct option.
1. Which of the following is true for Accuracy?
a. It is defined as the percentage of correct predictions out of all the observations.
b. It is defined as the percentage of true positive cases versus all the cases where the prediction is true.
c. It can be defined as the fraction of positive cases that are correctly identified.
d. None of the above
2. What is the formula for Accuracy?
(TP + TN) (FP + TN)
a. Accuracy = × 100% b. Accuracy = × 100%
(TP + TN + FP + FN) (TP + TN + FP + FN)
(FP + FN) (TP + FN)
c. Accuracy = × 100% d. Accuracy = × 100%
(TP + TN + FP + FN) (TP + TN + FP + FN)
Evaluation 319

