# Chapter 8 – Evaluation Class 10 Artificial Intelligence CBSE Question Bank

### Chapter 10 – Evaluation

1. Define Evaluation.

Answer: Moving towards deploying the model in the real world, we test it in as many ways as possible. The stage of testing the models is known as EVALUATION.

OR

Evaluation is a process of understanding the reliability of any AI model, based on outputs by feeding the test dataset into the model and comparing it with actual answers.

OR

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.

2. Which two parameters are considered for the Evaluation of a model?

Answer: Prediction and Reality are the two parameters considered for the Evaluation of a model.

The “Prediction” is the output which is given by the machine and the “Reality”is the real scenario when the prediction has been made.

3. What is True Positive?

• The predicted value matches the actual value
• The actual value was positive and the model predicted a positive value

4. What is True Negative?

• The predicted value matches the actual value
• The actual value was negative and the model predicted a negative value.

5. What is a False Positive?

• The predicted value was falsely predicted
• The actual value was negative but the model predicted a positive value
• Also known as the Type 1 error

6. What is False Negative?

• The predicted value was falsely predicted
• The actual value was positive but the model predicted a negative value
• Also known as the Type 2 error

#### Two (02) Mark Questions

1. What is meant by Overfitting of Data?

Answer: Overfitting is “the production of an analysis that corresponds too closely or exactly to a particular set of data, and may therefore fail to fit additional data or predict future observations reliably”.

(OR)

An Overfitted Model is a statistical model that contains more parameters than can be justified by the data. Here, to evaluate the AI model it is not necessary to use the data that is used to build the model. Because AI Model remembers the whole training data set, therefore it always predicts the correct label for any point in the training dataset. This is known as Overfitting

(OR)

Models that use the training dataset during testing, will always results in correct output. This is known as overfitting.

2. What is Accuracy? Mention its formula.

Answer: Accuracy is defined as the percentage of correct predictions out of all the observations.

A prediction is said to be correct if it matches reality. Here we have two conditions in which the Prediction matches with the Reality, i.e., True Positive and True Negative. Therefore, the Formula for Accuracy is

Where TP = True Positives, TN = True Negatives, FP = False Positives, and FN = False Negatives.

3. What is Precision? Mention its formula.

Answer: Precision is defined as the percentage of true positive cases versus all the cases where the prediction is true.

That is, it takes into account the True Positives and False Positives.

4. What is Recall? Mention its formula.

Answer: Recall is defined as the fraction of positive cases that are correctly identified.

5. Why is evaluation important? Explain.

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.

• Evaluation is important to ensure 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

6. How do you suggest which evaluation metric is more important for any case?

Answer: F1 Evaluation metric is more important in any case. F1 score sort maintains a balance between the precision and recall for the classifier. If the precision is low, the F1 is low and if the recall is low again F1 score is low.

The F1 score is a number between 0 and 1 and is the harmonic mean of precision and recall

When we have a value of 1 (that is 100%) for both Precision and Recall. The F1 score would also be an ideal 1 (100%). It is known as the perfect value for F1 Score. As the values of both Precision and Recall ranges from 0 to 1, the F1 score also ranges from 0 to 1.

7. Which evaluation metric would be crucial in the following cases? Justify your answer.

a. Mail Spamming

b. Gold Mining

c. Viral Outbreak

Answer: Here, Mail Spamming and Gold Mining are related to FALSE POSITIVE cases which are expensive at cost. But Viral Outbreak is a FALSE NEGATIVE case which infects a lot of people on health and leads to expenditure of money too for checkups.

So, False Negative cases (VIRAL OUTBREAK) are more crucial and dangerous when compared to FALSE POSITIVE cases.

(OR)

• a. If the model always predicts that the mail is spam, people would not look at it and eventually might lose important information. False Positive condition would have a high cost. (predicting the mail as spam while the mail is not spam)
• b. A model says that there is treasure at a point and you keep digging there, but it turns out that it is a false alarm. False Positive case is very costly. (predicting there is a treasure but there is no treasure)
• c. A deadly virus has started spreading and the model which is supposed to predict a viral outbreak does not detect it. The virus might spread widely and infect a lot of people. Hence, False Negative can be dangerous

8. What are the possible reasons for an AI model not being efficient? Explain.

Answer: Reasons of an AI model not being efficient:

• a. Lack of Training Data: If the data is not sufficient for developing an AI Model, or if the data is missed while training the model, it will not be efficient.
• b. Unauthenticated Data / Wrong Data: If the data is not authenticated and correct, then the model will not give good results.
• c. Inefficient coding / Wrong Algorithms: If the written algorithms are not correct and relevant, Model will not give desired output. Not Tested: If the model is not tested properly, then it will not be efficient.
• d. Not Easy: If it is not easy to be implemented in production or scalable.
• e. 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.

(Any three of the above can be selected)

• Give an example where High Accuracy is not usable.

SCENARIO: An expensive robotic chicken crosses a very busy road a thousand times per day. An ML model evaluates traffic patterns and predicts when this chicken can safely cross the street with an accuracy of 99.99%.

Explanation: A 99.99% accuracy value on a very busy road strongly suggests that the ML model is far better than chance. In some settings, however, the cost of making even a small number of mistakes is still too high. 99.99% accuracy means that the expensive chicken will need to be replaced, on average, every 10 days. (The chicken might also cause extensive damage to cars that it hits.)

• Give an example where High Precision is not usable.