Experts have designed these Class 10 AI Important Questions Chapter 7 Evaluation Class 10 Important Questions and Answers NCERT Solutions Pdf for effective learning.
Evaluation Class 10 Important Questions
Class 10 AI Evaluation Important Questions
Important Questions of Evaluation Class 10 – Class 10 Evaluation Important Questions
Very Short Answer Type Questions (1 mark each)
Question 1.
What is Evaluation?
Answer:
Evaluation is the process of determining the dependability of any Al model. Depending on the kind of model and its intended use.
Question 2.
What is Confusion Matrix?
Answer:
The result of the comparison between the prediction and reality can be recorded in a tabular structure which helps in measuring the performance of an AI model using the test data.
Question 3.
What do you mean by prediction?
Answer:
Prediction refers to the output produced by the Al model.
Question 4.
What is reality?
Answer:
Reality refers to the real scenario when the prediction has been made by a model.
Question 5.
What are the cases considered for evaluation?
Answer:
- True Positive
- True Negative
- False Positive
- False Negative
Question 6.
What do you mean by True Positive?
Answer:
True Positive refers to a condition that occurs when both predictions done by the AI model and reality are True or Yes.
Question 7.
What is True Negative?
Answer:
When both prediction and reality both are False or No, this condition is called True Negative.
Question 8.
Sarthak made a face mask detector system for which he had collected the dataset and used all the dataset to train the model. Then, he used the same data to evaluate the model which resulted in the correct answer all the time but was not able to perform with unknown dataset.
Name the concept.
Answer:
Overfitting
Question 9.
Which evaluation parameter takes into consideration all the correct predictions?
Answer:
Accuracy
Short Answer Type Questions (2 marks each)
Question 1.
What is the need for a confusion matrix?
Answer:
The confusion matrix allows to understand the prediction results by an AI model.
Question 2.
Mentions two conditions when prediction matches reality.
Answer:
The two conditions when prediction matches reality are:
- True Positive
- True Negative
Question 3.
Rozin is a student of class 10 AI. She wants to know the methods of evaluation. Support her with your answer.
Answer:
The evaluation methods are:
- Accuracy
- Precision
- Recall
- F1 Score
Question 4.
Which cases are taken into account by precision?
Answer:
True Positives and False Positives cases are taken into account by precision.
Question 5.
Which cases are taken into account by the recall method?
Answer:
True Positives and False Negatives cases taken into account by recall method.
Question 6.
Draw the confusion matrix for the following data
the number of true positive = 100
the number of true negative = 47
the number of false positive = 62
the number of false negative = 290
Answer:
Question 7.
People of a village are totally dependent on the farmers for their daily food items. Farmers grow new seeds by checking the weather conditions every year. An AI model is being deployed in the village which predicts the chances of heavy rain to alert farmers which helps them in doing the farming at the right time. Which evaluation parameter out of precision, recall and F1 Score is best to evaluate the performance of this AI model? Explain.
Answer:
Let us take each of the factor into consideration at once, If precision is considered, FN cases will not be taken into account, so it will be of great loss as if the machine will predict there will be no heavy rain, but if the rain occurred, it will be a big monetary loss due to damage to crops.
If only recall is considered, then FP cases will not be taken into account. This situation will also cause a big amount of loss, as all people of the village are dependent on farmers for food, and if the model predicts there will be heavy rain and the farmers may not grow crops, it will affect the basic needs of the people.
Hence, F1 Score is the best suited parameter to test this AI model, which is the balance between Precision and Recall.
Long Answer Type Questions (4 marks each)
Question 1.
There are 16 images, 9 are cat images and 7 are dog images. The cat images are positive cases and dog images are negative cases. The model identifies 5 cat images correctly and 3 cat images as dog images. Similarly, it identifies 4 of them correctly as dog images. Find the accuracy of the model.
Answer:
Total Predictions made: 5 + 3 + 4 = 12
Total Correct Predictions made: 5 + 4 = 9
So the accuracy is: 12 / 9 = 1.33 (Approx.)
Question 2.
Consider that there are 10 images. Out of these 7 are apples and 3 are bananas. Kirti has run the model on the images and it catches 5 apples correctly and 2 bananas correctly. What is the accuracy of the model?
Answer:
Total correct predictions are: 5 + 2 = 7
Total predictions made: 5 + 2
So accuracy is: 7 / 7 = 100%
The model does not predicted all of the images, but whatever predictions it makes are correct. Hence accuracy is $100 \%$.
Question 3.
In which situation evaluation metric is more important for any case?
Answer:
F1 evaluation metric is more significant in any case. Fl score sort of upholds a balance between is high again F1 score is high. The F1 score is a number between 0 and 1 and is the harmonic mean of precision and recall. The formula to determine F1 score is:
F1 Score = 2 × (Precision\timesRecall) /(Precision + Recall)
Question 4.
An AI model made the following sales prediction for a new mobile phone which they have recently launched:
(i) Identify the total number of wrong predictions made by the model.
(ii) Calculate precision, recall and F1 Score.
Answer:
(i) the total number of wrong predictions made by the model is the sum of false positive and false negative. FP + FN = 40 + 12 = 52
Question 5.
Automated trade industry has developed an AI model which predicts the selling and purchasing of automobiles. During testing, the AI model came up with the following predictions.
(i) How many total tests have been performed in the above scenario?
(ii) Calculate precision, recall and F1 Score.
Answer:
(i) TP = 60, TN = 10, FP = 25, FN = 5 60 + 25 + 5 + 10 = 100 total cases have been performed
(ii) (Note: For calculating Precision, Recall and F1 score, we need not multiply the formula by 100 as all these parameters need to range between 0 to 1)
Case Based Subjective Questions:
I. Read the following text and answer the following questions based on the same:
Question 1.
An AI mode the following predictions for Book Sales forecast. Calculate Accuracy, precision and recall for the following confusion matrix.
| Confusion Matrix | True Positives | True Negatives |
| Predicted Positive | 50 | 40 |
| Predicted Negative | 12 | 10 |
Answer:
