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c.  Legal Cases(Innocent until proven guilty)
                       d.  Fraud Detection
                       e.  Safe Content Filtering (like Kids YouTube)
                   Ans. a.   Precision: In spam detection, you generally want to minimise the number of legitimate emails incorrectly marked
                         as spam (False Positives). Precision is important here because it focuses on the accuracy of the positive predictions,
                         ensuring that when the model predicts an email is spam, it truly is spam.
                      b.   Recall: In cancer diagnosis, it's crucial to identify all the true cancer cases, even if it means some healthy people are
                         wrongly identified as having cancer (False Positives). Recall is more important here because it focuses on identifying
                         all actual positive cases (patients with cancer), which is critical to avoid missing any true positive cases.
                      c.   Precision: In a legal setting, particularly with the principle of "innocent until proven guilty," you want to minimize the
                         number of innocent people wrongly convicted (False Positives). Precision is important here because it ensures that
                         when a legal case is predicted to be guilty, the defendant is actually guilty.
                      d.   Recall: In fraud detection, it's important to catch all instances of fraud, even at the risk of flagging some legitimate
                         transactions as fraudulent. Recall is prioritized because missing out on fraudulent transactions (False Negatives) can have
                         significant consequences, whereas false alarms (False Positives) can usually be dealt with through further verification.
                      e.   Precision: For safe content filtering, you want to ensure that all flagged content is indeed inappropriate (False
                         Positives are more costly). Precision is key here because it focuses on making sure that when content is flagged as
                         unsafe, it truly is unsafe.
                 Assertion and Reasoning questions.
                 Direction: Questions 4-5, consist of two statements – Assertion (A) and Reasoning (R). Answer these questions by selecting
                 the appropriate option given below:
                 a.  Both A and R are correct and R is the correct explanation of A.
                 b.  Both A and R are correct but R is NOT the correct explanation of A.
                 c.  A is correct but R is incorrect.
                 d.  A is incorrect but R is correct.

                    4.  Assertion (A): Evaluation is the process of understanding the outcome of any AI model.
                       Reasoning (R): There can be different Evaluation techniques, depending on the type and purpose of the model.
                   Ans.  d.

                    5.  Assertion (A): The sum of the values in a confusion matrix's row represents the total number of instances for a given
                       actual class.
                       Reasoning (R): This enables the calculation of class-specific metrics such as precision and recall, which are essential for
                       evaluating a model's performance across different classes.
                   Ans.   a. Both A and R are correct and R is the correct explanation of A.

                                                       Unsolved Questions

                                                  SECTION A (Objective Type Questions)
                       uiz

                 A.  Tick ( ) the correct option.
                    1.  What is the primary purpose of model evaluation in machine learning?
                       a.  To reduce the size of the dataset
                       b.  To measure the model's performance and ensure it generalizes well to unseen data

                       c.  To increase the complexity of the model
                       d.  To avoid the need for real-world testing

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