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                     1.  What are the types of evaluation techniques?

                     2.  What characterises a model experiencing underfitting?



                       Accuracy and Error


              Let’s consider a scenario where, Aman and Priya went grocery shopping to buy a bag of 5 kg rice. Aman got `450
              and Priya got `500. The actual price of the rice was `550.
                 • Who is more accurate? Aman or Priya?
                Priya was more accurate, as her estimate is closer to the actual price.
                 • How much is the error for both Aman and Priya in estimating the price of a bag of rice?
                Error is the difference between the measured value and true value.
                Aman’s Error is `100 (550-400)
                Priya’s Error is `50 (550-500)
                Here, Priya is more accurate as her error is less than Aman’s error.

              The term Accuracy is defined as the evaluation metric that measures the total number of predictions that are
              correct by the model. It means how close the prediction is to the true value. The accuracy of the model and the
              performance of the model is directly proportional, which means better the performance of the model,  leads to
              higher accuracy in predictions.
              The term Error means the action that is inaccurate or wrong. It refers to the difference between a model’s prediction
              and the actual outcome. It quantifies how often the model makes mistakes. Based on the error, we choose the
              machine learning model that has the best performance for a specific dataset. Low error in a model 's performance
              signifies precise and reliable predictions.
                                                          Based on the present error, the AI model
                                                       parameters are fine tuned to reduce further error



                                                                     Predicted Value
                            Input Data            AI Model                less           =        Error

                                                                       Actual Value

              Note, high accuracy in a model indicates better model performance, but it may not reflect the true scenario,
              especially with an imbalance dataset.
              For example, you’re training a model to predict the approval of loan (Classification Task)

                 • Error: If the model predicts that the loan will not be approved but actually it’s approved by the bank, that’s an
                error. The error is the measurement of the difference between the prediction and the actual outcome.


                                                            Prediction
                    Approval of Loan         AI Model                     Loan Not Approved
                                                                                                             Not
                                                                                  –                        Approved
                                                              Actual
                                                                            Loan Approved




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