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UNIT 3





                                                                EVALUATING MODELS














                                  Learning Outcomes




                     •  Understanding Evaluation                          •  Splitting the Training Set Data for Evaluation
                     •  Evaluation Techniques                             •  Accuracy and Error
                     •  Evaluation Metrics for Classification             •  Confusion Matrix
                     •  Accuracy from Confusion Matrix                    •  Precision
                     •  Recall                                            •  F1 Score
                     •  Ethical Concerns Around Model Evaluation

                 Evaluation is the key step in the AI project cycle where the effectiveness, accuracy, and reliability of the model are
                 assessed to ensure it meets the project objectives and performs well in real-world scenarios.
                 After the designing of an AI model, it is important to evaluate the model to see that the model is designed as per
                 the need and is giving the desired process.


                         Understanding Evaluation


                 It is the evaluation phase of the AI project cycle, where we check whether the model can achieve required goals or
                 not. If the model fails to meet the requirements, we can modify either the model or the data. Once the developer
                 feels it is deployed in a real-world environment and handed over to the user. If the deployment stage is not
                 reached, the project is of no use.


























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