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•   Binary Classification: It refers to classification problems/tasks that have only two class labels. For example, Email
                    spam detection (spam or not), churn prediction (whether customers will stop doing business on a particular website).
                    In general, binary classification tasks involve 2 labels—normal and abnormal. For example, "no-spam" is a normal
                    condition and "spam" is an abnormal condition. Another example is that "tumour not detected" is a normal state
                    that includes medical testing and "tumour detected" is an abnormal condition.

                 •  Multi-class Classification: It implies those classification tasks that have more than two class labels. For example,
                    face classification, animal species classification, optical character recognition. In contrast to binary classification,
                    multiclass  classification  does  not  have  the  concept  of  normal  and  abnormal  classes.  Instead,  the  examples  are
                    classified as belonging to one of the several known classes.
                 The number of classes may be very large in some problems. For example, a model may tag a photo as belonging to one
                 among thousands of faces in a face recognition system. Text translation models are also a special type of multi-class
                 classification.


                 Binary Classification
                 As stated earlier, binary classification tasks have two class labels i.e. only two possible outcomes. The normal state is
                 assigned the class label 0 and the abnormal state is assigned the class label 1. For example, the following shows a graph
                 for classifying areas in a city based on covid/non-covid zones:



                                    8                                                               0
                                                                                                    1
                                    6


                                    4

                                    2


                                    0


                                   –2

                                   –4


                                   –6


                                           –12      –10     –8      –6       –4      –2      0       2


                 Several algorithms are used for binary classification. These include:
                 •  Logistic Regression

                 •  k-Nearest Neighbors
                 •  Decision Trees
                 •  Support Vector Machine
                 Out of these algorithms, let us study about ‘Logistic Regression’.








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