Bias and accuracy in machine learning
Lesson 7 / 8
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Lesson outcome
I can describe the impact of data on ML models and explain bias in ML model predictions.
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The type, quality and amount of data used significantly affect how accurate a machine learning (ML) model is.
ML models require both training data and separate test data to ensure reliability.
Bias is introduced into ML models when the data is unrepresentative or contains stereotypes.
Use large, representative data sets and diverse perspectives to reduce bias and improve ML model fairness and accuracy.
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This content is by Cashar and published under an open licence.