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3.  In Train-test split, the training subset is used to make the model learn patterns from the data,
                    comprising 50% to 60% of the dataset.                                                ……….……................
                  4.  In underfitting, the model is too complex and performs poorly on both training and test data.   ……….……................

                  5.  Model evaluation is a process that critically examines a model to assess its performance.    ……….……................

              D.  Match the following:

                  1.  Classification                a.  Error Matrix
                  2.  Confusion Matrix              b.  Type 2 Error
                  3.  False Positive                c.  Classification Model
                  4.  False Negative                d.  Supervised Learning

                  5.  F1 Score                      e.  Type 1 Error

                                               SECTION B (Subjective Type Questions)

              A.  Short answer type questions.
                  1.  Why is it important to maintain a balance between bias and variance in a machine learning model?

                Ans.  It’s important to maintain a balance between bias and variance in machine learning model to ensure the model performs
                    consistently on both training and test data.
                  2.  Where should we use recall?
                Ans.  Recall is generally used for unbalanced dataset, when dealing with the False Negatives become important and the
                    model needs to reduce the FNs as much as possible.
                  3.  What is the primary benefit of using the Train-Test Split technique in model evaluation?
                Ans.  The primary benefit of using the Train-Test Split technique in model evaluation is that it gives an unbiased estimate of
                    model performance on new data.

                  4.  What is the significance of the False Negative (FN) in a confusion matrix?
                Ans.  A False Negative (FN) indicates that the model incorrectly predicted a negative outcome, even though the actual
                    outcome was positive. It can be critical in scenarios like medical diagnosis.
                  5.  How does classification accuracy differ when the dataset is unbalanced?

                Ans.  When the dataset is unbalanced, classification accuracy can be misleading, as the model may predict the majority class
                    correctly but fail on the minority class.

              B.  Long answer type questions.

                  1.  Explain the concept of a confusion matrix and its components. How is it used to evaluate a classification model?
                Ans.  A confusion matrix is a performance evaluation tool used in machine learning to summarieshe performance of a
                    classification model. It is a tabular representation that compares the actual labels (true outcomes) with the predicted
                    labels (model predictions). The table is made with 4 different combinations of predicted and actual values in the form
                    of 2×2 matrix.
                    To understand the confusion matrix, let’s understand the following terms:
                    •  Positive: The prediction is positive for the scenario. For example, if there will be snowfall.
                    •  Negative: The prediction is negative for the scenario. For example, there will be no snowfall.
                    •   True  Positive:  The  predicted  value  matches  the  actual  value  i.e.;  the  actual  value  was  positive  and  the  model
                       predicted a positive value.
                    •   True Negative: The predicted value matches the actual value i.e.; the actual value was negative and the model
                       predicted a negative value.

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