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• Fairness - Fairness ensures that models treat all groups equally. The evaluation processes must account for
                   fairness to prevent models from producing discriminatory results.
                    • Transparency - Sometimes, evaluation models lack clarity, making it difficult to understand how predictions are
                   made. A transparent approach clearly explains how metrics are chosen and how results are derived.
                    • Accountability - It is crucial to take responsibility for the choice of evaluation metrics and their outcomes.
                   Providing clear reasoning behind metric selection helps ensure ethical decision-making and builds trust.
                    • Privacy - Using real-world data for evaluation often involves sensitive personal information, raising concerns
                   about privacy. Measures should be in place to ensure that individual data is protected during the evaluation
                   process.
                    • Data Protection - Protecting the data used in model evaluation is essential to prevent misuse or unauthorised
                   access.


                                                                                             21 st  Century   #Media Literacy
                                                                                                 Skills
                           Video Session


                      Watch the video on "Precision, Recall, F1 score, True Positive" at the given link:
                      https://www.youtube.com/watch?v=2osIZ-dSPGE or scan the QR code and answer  the
                      following question:
                      What did you learn about model evaluation from the video?








                           At a Glance


                       • Model evaluation is the process of applying various metrics to assess a machine learning model’s performance.
                       • Training subset is used to make the model learn patterns from the data. It comprises 70% to 80% of the dataset.
                       • Testing subset is used to evaluate a model on the unseen data. It comprises of 20% to 30% of the dataset.
                       • The ML algorithm is trained using the training data. This involves feeding the data into the algorithm, which learns
                       patterns and relationships to create a model.
                       • Evaluation techniques involve assessing a machine learning model’s performance on training and test data.
                       • The  accuracy  of  the  model  and  the  performance  of  the  model  is  directly  proportional,  that  means  better  the
                       performance of the model, higher is the accuracy of the 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.
                       • Classification is a type of supervised learning in machine learning where the goal is to predict the categorical label
                       or class of a given input based on historical data.
                       • Classification is part of supervised machine learning in which we put labelled data for training.
                       • Some popular metrics used for classification models are confusion matrix, classification accuracy, precision and
                       recall.
                       • The classification outcomes based on the different values of actual and predicted labels are True Positive, True
                       Negative, False Positive and False Negative.
                       • Precision is the ratio of True Positive cases to All predicted positive cases.
                       • F1 score can be defined as the measure of balance between precision and recall.



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