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4.   False Positive is           error.
                     5.   In clustering, when you can quantify the metric manually, it is called    .
                 C.   State whether the following statement is True or False.

                     1.   The output predicted by linear regression is a discrete value.
                     2.   Imbalanced clusters can have elliptical shape.
                     3.   In classification, the AI model needs training data to understand how a given input variable is
                          related to the class.
                     4.   Precision is the proportion of positive cases that are correctly identified.
                     5.   The K-means algorithm identifies k number of high-density areas, and then assigns every
                          data point to the nearest cluster.
                 D.   Match the following.
                     1.   Medical Imaging Analysis           a.     Binary Classification
                     2.   False Positive                     b.   Application of Clustering
                     3.   Handwriting Recognition            c.     Recommender Systems
                     4.   Decision Trees                     d.   Indicates False Alarm
                     5.   Collaborative Filtering            e.   Application of Classification


                                                  SECTION B (Subjective Type Questions)
                 A.   Short answer type questions.
                     1.   What is meant by generalisation of the K-means algorithm?
                     2.   If the Accuracy of an AI model is high, does it mean that the model is working ideally?
                     3.     Group the following algorithms into Supervised and Unsupervised Learning:
                          Logistic  Regression,  K-  means  Clustering,  K-Nearest  Neighbors,  Decision  Trees,  Hierarchal  clustering,  Support
                          Vector Machine.
                     4.   Find out the working of a recommender system. What technique does it use?
                     5.   Differentiate between centroid-based and density-based clustering.
                 B.   Long answer type questions.
                     1.   What is classification? Explain the two types of Classification with examples.
                     2.   What is a Confusion Matrix? Why do we need a confusion matrix? Mention 2 benefits of the confusion matrix also.
                     3.   Explain the different types of clustering.
                     4.   Differentiate between Linear and Logarithmic regression.
                     5.   An AI model studies data samples of 2000 cases and predicts forest fires in the Amazon forests. The prediction
                          categories and their number are given as follows:
                          •     Model predicted forest fire and the event took place            893
                          •     Model predicted no forest fire and no such event happened       480
                          •     Model predicted no forest fire but such an event occurred       (to be calculated)
                          •     Model predicted forest fire but no such event occurred          317
                               Create a confusion matrix using the above information.
                               Also calculate Accuracy, Precision, Recall and F1 Score.

                 C.   Competency-based/Application-based questions:
                     1.   Sonali is studying supervised learning algorithms. She is confused for a particular dataset when to apply Regression
                          and classification algorithms. Help her understand when to apply that particular supervised algorithm.
                     2.   Now Sonali is studying confusion matrix. She does not understand Type I and Type II errors. Help Sonali understand
                          the meaning of these terms.


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