Page 323 - AI Ver 1.0 Class 10
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6.  Unauthenticated data can be data collected from ……….……................ resources.
                    7.  ……….……................ is the real scenario of the situation.
                    8.  When the prediction matches with the reality this condition is called ……….……................ .

                 C.  State whether these statements are true or false.

                    1.  Model Evaluation is not an important stage in AI Model.                            ……….……................
                    2.  The lack of training data could lead to inefficiency of the AI Model.              ……….……................

                    3.  The testing data and the training data should be totally different.                ……….……................
                    4.  Prediction and Reality are two important parameters for Model Evaluation.          ……….……................

                    5.  False Negative is also called Type 1 Error.                                        ……….……................

                 D.  Match the following:

                    1.  Reality                       a.  Error Matrix
                    2.  Prediction                    b.  Actual Values
                    3.  Confusion Matrix              c.  Model Evaluation method
                    4.  Columns in Confusion Matrix   d.  Model Evaluation Parameter

                    5.  False Positive                e.  Type 1 error

                                                  SECTION B (Subjective Type Questions)

                 A.  Short answer type questions:

                    1.  What is Model Evaluation? Why it is important to evaluate a model?
                   Ans.  Model Evaluation is the last stage of the AI Project development cycle. It is the stage of testing the model where testing
                       data is given to the system and the output generated is evaluated with the actual result to see the accuracy of the
                       output and the reliability of the AI model. It is important to evaluate the model to see that the model is designed as
                       per the need and is giving the desired process.

                    2.  Give an example where high Accuracy is not useful?
                   Ans.  A robot programmed to cap 2000 bottles in a factory has 99.99% accuracy. If there occur some errors in the robot due
                       to any reason then this will create a huge loss in the factory.
                    3.  Give an example where High Precision is not useful?
                   Ans.  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.
                    4.  Create a confusion matrix for the following problem?
                       Let us imagine that we have an AI model which identifies an apple from a mango. Following are the cases

                                Predicted                 Reality
                          Yes                        No
                          Yes                        No
                          Yes                        Yes
                          No                         Yes
                          No                         Yes
                          Yes                        Yes
                          No                         No

                   Ans.  The Confusion Matrix

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