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AI Models

            There are mainly three types of AI models: Regression, Classification and Clustering. Let us learn about them in
            detail.


            Regression
            Regression is an example of rule-based AI models. This is a type of Rule-
            Based AI model. In regression, the  algorithm  generates a  mapping
            function from the given data, as shown by the solid line in the given
            graph. The blue dots shown in the graph are the data values and the
            solid line here represents the mapping done for them. With the help of

            this mapping function, we can predict the future data. It works with
            continuous data.


                                                     Classification
                                                     Classification  is  another  rule-based  AI  model.  It  is  a  systematic
                                                     grouping of observations in categories, something like categorising
                                                     plants, animals in different taxonomies by biologists. In classification,

                                                     you teach the machine to perform with labelled data. Testing data is
                                                     then  classified  as  one  of  the  labels  of  the  training  dataset.  The
                                                     algorithm is able to determine which set a given data point belongs
                                                     to by means of a classification function represented by the dotted
            line. The model classifies datasets according to the rules given to it.


            Clustering
            Clustering is a machine learning approach where the machine partitions
            the dataset into different clusters or categories based on machine-
            generated algorithms. The data fed to such a model is usually unlabelled

            or random and thus the developer feeds in the data directly into the
            machine and instructs it to build its own algorithm. The machine then
            forms a pattern or cluster based on training data and groups those that
            follow the same pattern. The best clustering is the one that minimizes the error.  Clustering works on discrete dataset.



                            Task




                 1. Define classification and its significance in AI.



                 2. Explain the use of labeled data in classification models.






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