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For example:
                      point = (5, 10)
                      x, y = point
                      In the given code, the values (5, 10) are unpacked from the point tuple and assigned to variables x and y respectively
                   5.  What is the use of NumPy?
                 Ans.  NumPy is the short form of Numerical Python. It is a fundamental library in Python that is used for performing
                      numerical computation. It provides support for arrays, matrices, and a variety of mathematical functions to operate
                      on these data structures efficiently. Its array-based data structures and operations execution makes it very useful for
                      various applications, such as data analysis, machine learning, scientific computing, etc.


              C.   Competency-based/Application-based question:                             #Interdisciplinary

                      In ABC company the employees were being lectured about the evaluation of an AI model. A student was confused
                      between testing and evaluation. Help him solve his problem by giving concrete points and letting him know the
                      importance of various metrices for evaluation of an AI model.
                 Ans.   First let us clarify the student’s doubt that testing is actually running the test cases (sample inputs to check if the
                      results are accurate) on the developed model whereas evaluation is finding inference (understanding) out of what
                      results have come, when test cases are run.
                      Testing is actually running the model in different scenarios whereas evaluation is preparing a report of what testing
                      results are pointing out.
                      In machine learning, metrics are used to analyse and measure the performance of models. Metrics evaluate how well
                      the model makes predictions, allowing us to better understand its usefulness and identify areas for improvement.

                      Some important uses of metrics are as follows:
                         Model evaluation: Metrics assist in determining how well a model works on a specific dataset. Accuracy, precision,
                         recall, F1-score, and AUC-ROC are some of the most commonly used evaluation metrics.
                         Comparison: Metrics enable the comparison of many models or algorithms to identify which one performs best
                         for a given task.
                         Validation: During model construction, metrics are used to assess the model's performance on distinct training
                         and test sets to ensure that it generalizes well to new data.
                         Optimisation: Metrics assist hyperparameter tuning and feature selection to optimize model performance.



                                                     Unsolved Questions

                                                 SECTION A (Objective Type Questions)
                        uiz
              A.  Tick ( ) the correct option.

                  1.   Python is classified as which type of language?
                      a.   Compiled                                    b.  Interpreted
                      c.   Assembly                                    d.  Machine

                  2.   Which feature of Python significantly reduces the need to write code from scratch?
                      a.   Dynamic typing                              b.  Open source
                      c.   Extensive standard library                  d.  Cross-platform compatibility

                  3.   Which of the following characters is NOT part of Python's character set?
                      a.   @                                           b.  $
                      c.   ~                                           d.  §


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