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                      1.   Classification groups data into        .
                      2.  In logistic regression, 1 means the event         and 0 means that the event             .
                      3.   In  Multiclass  Classification,  data  samples  are  classified  as  belonging  to  one of  the
                         classes.
                      4.   A classification model tries to map the input variable function to discrete output variables.
                         (State True or False)
                      5.  Name a binary classification algorithm other than Logistic regression.







                        Confusion Matrix—Evaluating a Classification Model

                 In the field of machine learning, a confusion matrix (NxN matrix) is used to validate the performance of a classification
                 model i.e. how good are the classifier’s predictions, where N is the number of target classes. The confusion matrix
                 compares the actual target values with those predicted by the classifier. This tells us how well the classification model is
                 performing and what kind of error it is making.
                 For a binary classification problem, we would have a 2x2 matrix (N = 2 classes) which looks as follows:


                                                                                  Reality
                                  The Confusion

                                       Matrix
                                                                       Yes                      No



                                                   Yes           True Positive (TP)       False Positive (FP)

                               Prediction

                                                   No           False Negative (FN)      True Negative (TN)


                 (The rows denote the predicted values given by the AI classifier model and the columns denote the actual values of
                 the target variable. Let us understand the remaining terms.

                 True Positive (TP)
                 •  The predicted value tallied with the actual value.
                 •  The actual value was positive and the classification model also predicted positive.
                 •  As the values match, there is no error.
                 True Negative (TN)

                 •  The predicted value tallied with the actual value.
                 •  The actual value was negative and the classification model also predicted negative.
                 •  As the values match, there is no error.

                 False Positive (FP)
                 •  The predicted value doesn’t tally with the actual value.

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