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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

                  2.  Which evaluation technique involves dividing the dataset into training and testing subsets?
                    a.  Precision                                      b.  Gradient Boosting
                    c.  Train-test split                               d.  Recall

                  3.  In which scenario is a model said to be "underfitting"?
                    a.  The model performs poorly on both training and test sets

                    b.  The model performs well on both training and test sets
                    c.  The model memorizes the training data but fails to generalize
                    d.  The model performs well on the training set but poorly on the test set

                  4.  What is True Positive (TP) in the confusion matrix?
                    a.  When the model predicts a negative value correctly

                    b.  When the model predicts a negative value incorrectly
                    c.  When the model predicts a positive value incorrectly

                    d.  When the model predicts a positive value correctly

                  5.  In a confusion matrix, the rows represent the ………………………. values of the target variable.
                    a.  Predicted                                      b.  Actual
                    c.  Desired                                        d.  Assigned

                  6.  Which metric is most suitable when you want to minimise false positives?
                    a.  Accuracy                                       b.  Precision
                    c.  Recall                                         d.  F1 Score


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