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4.  Explain F1 Score.
                   Ans.  F1 Score gives a measure of the balance between precision and recall. A good F1 score means that both the false
                        positives and false negatives are low, so the AI model is identifying the real threats properly and is not giving any
                        false alarms. F1 score ranges between 0 and 1. If the F1 score is 1 (100%), the model is considered perfect, and if
                        the score is 0, that means that the model has failed completely.

                     5.  Differentiate between Regression and Classification, Also, draw the graphical representation of each.
                   Ans.
                                         Regression                                  Classification
                         This  algorithm  is  used  to  predict  values  such  as  Classification algorithms are used to group values
                         price, salary, age, etc.                     such as Male/Female, True/False, Spam/Not Spam
                                                                      into classes.

                         Example – Linear Regression                  Example – Logistic Regression
                         The graph is a straight line.                The graph is a Sigmund curve.

                           120                                                1
                                                                              0.9
                           100
                                                                              0.8
                            80                                                0.7
                           Marks (Y)   60                                     0.6
                                                                              0.5
                            40                                                0.4
                                                                              0.3
                            20
                                                                              0.2
                            0                                                 0.1
                             0   1  2   3  4   5  6   7  8   9  10
                                          No. of Hours Studied (X)            0
                                                                               –6  –4  –2   0   2   4    6
              B.   Long answer type questions.
                     1.  Give any 3 real life applications of clustering.
                   Ans.  a.  Document  Classification/Organization:  The  algorithm  views  the  text  and  groups/clusters  it  into  different
                         topics. This technique allows to quickly group and organize similar documents using the characteristics given in
                         the paragraph.
                       b.  Recommendation Systems: Recommendation systems are widely used by Amazon, Netflix, Flipkart etc. to provide
                         automated and personalized recommendations for products, services and information. The technology behind the
                         recommendation engines is called collaborative filtering. A cluster is formed based on the preferences of customers.
                         Customers within each cluster get recommendations computed at the cluster level.
                       c.  Medical Imaging Analysis: Clustering is used to match patterns in the images and identify cancerous datasets. A
                         mix of both cancerous and non-cancerous datasets are analysed by the clustering algorithms to understand the
                         different characteristics present in the dataset, producing resultant clusters.
                     2.   It  is  estimated  that  around  500,000  earthquakes  occur  each  year,  though  most  can  be  detected  with  current
                        instruments. About 100,000 of these are felt by humans. An earthquake causes injury and loss of life and damage
                        to property/buildings. The after-effects may result in disease, lack of basic necessities, mental trauma such as panic
                        attacks, depression for survivors. Considering all the possible situations make a Confusion Matrix for the given
                        situation.
                   Ans.  Case 1: Did an Earthquake occur? Prediction – Yes Reality Yes
                        True Positive
                        Case 2: Did an Earthquake occur? Prediction – No Reality No
                        True Negative
                        Case 3: Did an Earthquake occur? Prediction – Yes Reality No
                        False Positive

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