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C.   State whether the following statements are true or false.
                 .    yperparameters are internal to an AI model.
                 .    here is no such thing as an ideal split percentage.

                 .    ross validation is used for evaluating machine learning models on a large sample of data.
                 .    very pro ect starts  ith a business understanding.
                 .    he data collection stage, that begins  ith the initial version of the prepared data set,

                     focuses on constructing predictive or descriptive models.
                 .    egression predicts a real number as the last component of the result.
                 .    he decision tree categorises the dataset into groups based on several criteria.

            D.   Match the Following.
                 .                                    a.    roblem Decomposition
                 .    est dataset                     b.   sensitive to outliers
                 .    eal life problems               c.   brainstorming
                 .   Ideate in D                      d.   quantity
                 .    egression functions predict     e.    valuation stage


                                             SECTION B (Subjective Type Questions)
            A.   Short answer type questions.
                 .    hat is a loss function   ame any t o  egression  oss functions.
              Ans.    A loss function is used by machines to learn. It's a  ay of determining ho   ell a certain algorithm models the
                     data. If the forecasts are too far off from the actual findings, the loss function  ill return a very large number.
                      oss function learns to lo er prediction error over time  ith the help of some  optimisation ob ective function.
                      egression  oss functions are      and    .
                 .    an     be a negative value   hy  hy not   ive the equation to calculate    .
              Ans.        cannot be a negative value.  he difference bet een the predicted and actual values can be negative.  o ever,
                     these differences are squared.  ence, all results are either positive or  ero.
                                                                        p
                                                                    y  – y )
                                                          MSE    i=i  i  i
                                                                     n
                 .    hat is meant by the iterative nature of the problem solving methodology
              Ans.    As data scientists have a better understanding of the data and models, they typically return to a prior stage to
                     make changes.  odels aren't built once, deployed, and forgotten about  instead, they're constantly refined and
                     adapted to changing situations through feedback, refinement, and redeployment. As a result, both the model and
                     labour that goes into it can continue to add value to the business for as long as the solution is required.  ence the
                     problem solving methodology is iterative in nature.

                 .     he lo er the value of    , the better is the model . Do you agree   hy  hy not
              Ans.        is calculated for a regression line as the average of the sum of squares for all data points.     is used to see
                     ho  close an estimate or forecast is to an actual value.  he lo er the    , the closer the forecast is to the actual.
                      o, smaller values indicate a better model.
                 .    hat is meant by feedback of model effectiveness
              Ans.     he organisation receives feedback on the model's effectiveness and impact on the environment in  hich it  as
                     deployed  by  collecting  findings  from  the  implemented  model.  Data  scientists  utilise  the  feedback  to  improve
                     the model's accuracy and utility by analysing it.  hey can automate any or all of the feedback gathering, model
                     assessment, refining, and redeployment phases to speed up the model refresh process and improve results.



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