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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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