Page 357 - AI Ver 3.0 Class 11
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B.  Fill in the blanks.
                    1.                have the capacity to extract information from limited number of datasets.

                    2.                is a technique used to reduce the number of features or variables in a dataset while preserving the
                       most important information.
                    3.  If data points are closer to the line of best fit (less residual error), it means the correlation between the two variables is
                                      .

                    4.                in statistics, are data points that are significantly different from other observations.
                    5.  In K-means algorithms, the value of K, which represents the number of clusters, must be selected   .

                    6.                uses KNN to detect data points which are substantially distinct from the remaining portion of the data.
                    7.                is a clustering model in which we try to fit the data on the probability that it can belong to the same
                       distribution.

                    8.               is driven by the principle that objects within a group should be very similar to each other, but very
                       different from the objects outside.
                    9.                uses a single independent variable for forecasting an outcome of a numerical dependent variable.

                    10.  In            , the algorithm learns from labelled data, where each training example is paired with a corresponding
                       target label.

                 C.   State whether these statements are true or false.
                    1.  In Supervised Learning, during training, the algorithm adjusts its parameters to minimise the
                       difference between predicted and actual labels.

                    2.  No correlation means that there is no relationship between two variables.
                    3.  Bias in training data can lead to skewed predictions.

                    4.  Non-parametric learning makes no assumptions about the original data distribution.

                    5.  Clustering is used to identify patterns and structures in unlabelled data sets.
                    6.  Single linear regression demonstrates a connection between two or more independent variables
                       and the associated variables that are dependent.
                    7.  The K-means algorithm identifies 5 centroids.
                    8.  Market segmentation involves dividing a broad consumer or business market into sub-groups of
                       consumers based on some type of shared characteristics.
                    9.  Clustering algorithms group people with similar characteristics who are most likely to buy your
                       product or service.
                    10.  KNN retains few pieces of training data, requiring little memory resources.


                                                  SECTION B (Subjective Type Questions)

                 A.  Short answer type questions:
                    1.  What is Machine learning?
                   Ans.  Machine learning is an application of Artificial Intelligence (AI) that enables systems to learn and improve automatically
                       from experience without the need for explicit programming. It focuses on the development of computer programs that
                       can access data and use it to learn for itself. Data is critical for machine learning to work. The more data the machine is
                       given (assuming that the data is reliable), the more accurate is its prediction.

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