CBSE Question Bank: Evaluation | Artificial Intelligence for Class 10 PDF Download

 Page 1


 
CBSE Question Bank – AI – Class 10 – Chapter- 8 Evaluation 1 
 
 
CBSE | DEPARTMENT OF SKILL EDUCATION  
ARTIFICIAL INTELLIGENCE 
QUESTION BANK – CLASS 10 
 
CHAPTER 8: EVALUATION 
 
One (01) Mark Questions 
1.  Define Evaluation. 
Moving towards deploying the model in the real world, we test it in as many ways 
aspossible. 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 Evaluation of a model? 
 
Prediction and Reality are the two parameters considered for 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 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 
 
  
Page 2


 
CBSE Question Bank – AI – Class 10 – Chapter- 8 Evaluation 1 
 
 
CBSE | DEPARTMENT OF SKILL EDUCATION  
ARTIFICIAL INTELLIGENCE 
QUESTION BANK – CLASS 10 
 
CHAPTER 8: EVALUATION 
 
One (01) Mark Questions 
1.  Define Evaluation. 
Moving towards deploying the model in the real world, we test it in as many ways 
aspossible. 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 Evaluation of a model? 
 
Prediction and Reality are the two parameters considered for 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 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 
 
  
 
CBSE Question Bank – AI – Class 10 – Chapter- 8 Evaluation 2 
 
 
Two (02) Mark Questions 
 
1.   What is meant by Overfitting of Data? 
 
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. 
Accuracy is defined as the percentage of correct predictions out of all theobservations. 
A prediction is said to be correct if it matches reality. Here we have twoconditions in which 
the Prediction matches with the Reality, i.e., True Positive and True Negative. Therefore, 
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. 
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. 
 
  
Page 3


 
CBSE Question Bank – AI – Class 10 – Chapter- 8 Evaluation 1 
 
 
CBSE | DEPARTMENT OF SKILL EDUCATION  
ARTIFICIAL INTELLIGENCE 
QUESTION BANK – CLASS 10 
 
CHAPTER 8: EVALUATION 
 
One (01) Mark Questions 
1.  Define Evaluation. 
Moving towards deploying the model in the real world, we test it in as many ways 
aspossible. 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 Evaluation of a model? 
 
Prediction and Reality are the two parameters considered for 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 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 
 
  
 
CBSE Question Bank – AI – Class 10 – Chapter- 8 Evaluation 2 
 
 
Two (02) Mark Questions 
 
1.   What is meant by Overfitting of Data? 
 
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. 
Accuracy is defined as the percentage of correct predictions out of all theobservations. 
A prediction is said to be correct if it matches reality. Here we have twoconditions in which 
the Prediction matches with the Reality, i.e., True Positive and True Negative. Therefore, 
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. 
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. 
 
  
 
CBSE Question Bank – AI – Class 10 – Chapter- 8 Evaluation 3 
 
 
 
4. What is Recall? Mention its formula. 
Recall is defined as the fraction of positive cases that are correctly Identified.   
 
5. Why is evaluation important? Explain. 
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. 
 
? 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? 
F 1 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 
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 case (VIRAL OUTBREAK) are more crucial and dangerous when 
compared to FALSE POSITIVE cases. 
       (OR) 
Page 4


 
CBSE Question Bank – AI – Class 10 – Chapter- 8 Evaluation 1 
 
 
CBSE | DEPARTMENT OF SKILL EDUCATION  
ARTIFICIAL INTELLIGENCE 
QUESTION BANK – CLASS 10 
 
CHAPTER 8: EVALUATION 
 
One (01) Mark Questions 
1.  Define Evaluation. 
Moving towards deploying the model in the real world, we test it in as many ways 
aspossible. 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 Evaluation of a model? 
 
Prediction and Reality are the two parameters considered for 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 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 
 
  
 
CBSE Question Bank – AI – Class 10 – Chapter- 8 Evaluation 2 
 
 
Two (02) Mark Questions 
 
1.   What is meant by Overfitting of Data? 
 
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. 
Accuracy is defined as the percentage of correct predictions out of all theobservations. 
A prediction is said to be correct if it matches reality. Here we have twoconditions in which 
the Prediction matches with the Reality, i.e., True Positive and True Negative. Therefore, 
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. 
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. 
 
  
 
CBSE Question Bank – AI – Class 10 – Chapter- 8 Evaluation 3 
 
 
 
4. What is Recall? Mention its formula. 
Recall is defined as the fraction of positive cases that are correctly Identified.   
 
5. Why is evaluation important? Explain. 
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. 
 
? 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? 
F 1 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 
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 case (VIRAL OUTBREAK) are more crucial and dangerous when 
compared to FALSE POSITIVE cases. 
       (OR) 
 
CBSE Question Bank – AI – Class 10 – Chapter- 8 Evaluation 4 
 
 
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 saying that there exists treasure at a point and you keep on 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. 
 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) 
 
9. Answer the following: 
 
? 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. 
 
Example: “Predicting a mail as Spam or Not Spam” 
False Positive: Mail is predicted as “spam” but it is “not spam”. 
False Negative: Mail is predicted as “not spam” but it is “spam”. 
Of course, too many False Negatives will make the spam filter ineffective but False 
Positives may cause important mails to be missed and hence Precision is not usable. 
 
  
Page 5


 
CBSE Question Bank – AI – Class 10 – Chapter- 8 Evaluation 1 
 
 
CBSE | DEPARTMENT OF SKILL EDUCATION  
ARTIFICIAL INTELLIGENCE 
QUESTION BANK – CLASS 10 
 
CHAPTER 8: EVALUATION 
 
One (01) Mark Questions 
1.  Define Evaluation. 
Moving towards deploying the model in the real world, we test it in as many ways 
aspossible. 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 Evaluation of a model? 
 
Prediction and Reality are the two parameters considered for 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 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 
 
  
 
CBSE Question Bank – AI – Class 10 – Chapter- 8 Evaluation 2 
 
 
Two (02) Mark Questions 
 
1.   What is meant by Overfitting of Data? 
 
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. 
Accuracy is defined as the percentage of correct predictions out of all theobservations. 
A prediction is said to be correct if it matches reality. Here we have twoconditions in which 
the Prediction matches with the Reality, i.e., True Positive and True Negative. Therefore, 
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. 
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. 
 
  
 
CBSE Question Bank – AI – Class 10 – Chapter- 8 Evaluation 3 
 
 
 
4. What is Recall? Mention its formula. 
Recall is defined as the fraction of positive cases that are correctly Identified.   
 
5. Why is evaluation important? Explain. 
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. 
 
? 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? 
F 1 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 
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 case (VIRAL OUTBREAK) are more crucial and dangerous when 
compared to FALSE POSITIVE cases. 
       (OR) 
 
CBSE Question Bank – AI – Class 10 – Chapter- 8 Evaluation 4 
 
 
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 saying that there exists treasure at a point and you keep on 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. 
 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) 
 
9. Answer the following: 
 
? 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. 
 
Example: “Predicting a mail as Spam or Not Spam” 
False Positive: Mail is predicted as “spam” but it is “not spam”. 
False Negative: Mail is predicted as “not spam” but it is “spam”. 
Of course, too many False Negatives will make the spam filter ineffective but False 
Positives may cause important mails to be missed and hence Precision is not usable. 
 
  
 
CBSE Question Bank – AI – Class 10 – Chapter- 8 Evaluation 5 
 
 
Four (04) Mark Questions 
 
1. Deduce the formula of F1 Score? What is the need of its formulation? 
 
The F1 Score, also called the F score or F measure, is a measure of a test’s accuracy. 
It is calculated from the precision and recall of the test, where the precision is the number 
of correctly identified positive results divided by the number of all positive results, 
including those not identified correctly, and the recall is the number of correctly identified 
positive results divided by the number of all samples that should have been identified as 
positive. 
The F1 score is defined as the weighted harmonic mean of the test’s precision and recall. 
This score is calculated according to the formula. 
 
Formula: 
 
Necessary: 
F-Measure provides a single score that balances both the concerns of precision and recall in 
one number. 
A good F1 score means that you have low false positives and low false negatives, so you’re 
correctly identifying real threats, and you are not disturbed by false alarms. 
An F1 score is considered perfect when it’s 1, while the model is a total failure when it’s 0. 
F1 Score is a better metric to evaluate our model on real-life classification problems and 
when imbalanced class distribution exists. 
 
2. What is a confusion matrix? Explain in detail with the help of an example. 
 
Confusion Matrix: 
A Confusion Matrix is a table that is often used to describe the performance of a 
classification model (or "classifier") on a set of test data for which the true values are 
known. 
(or) 
 
A 2x2 matrix denoting the right and wrong predictions might help us analyse the rate of 
success. This matrix is termed the Confusion Matrix. 
 
Evaluation of the performance of a classification model is based on the counts of test 
records correctly and incorrectly predicted by the model. 
 
Therefore, Confusion Matrix provides a more insightful picture which is not only the 
performance of a predictive model, but also which classes are being predicted correctly and 
incorrectly, and what type of errors are being made. 
 
The confusion matrix is useful for measuring Recall (also known as Sensitivity), Precision, 
Accuracy and F1 Score.