Machine learning bias refers to errors that can occur in a machine learning model due to assumptions made during the modeling process. A model is biased when it makes assumptions about the data that lead to predictions that are systematically inaccurate.
Machine Learning Bias in practic
Machine learning bias can manifest in various ways. First, in the context of model bias, it occurs when the model's assumptions about the data structure are incorrect, causing the model to either underfit or overfit the data. Underfitting happens when the model is too simple to capture relevant patterns in the data, while overfitting happens when the model is too complex and captures irrelevant patterns or noise.
Second, in the context of data bias, it happens when the data used to train the model is not representative of the population the model will be applied to. For instance, if a facial recognition model is trained on a dataset consisting mostly of male faces, the model would likely perform poorly when trying to recognize female faces.
It's crucial to detect and handle bias because it can significantly affect the accuracy of the model's predictions and can lead to unfair or discriminatory outcomes. Handling bias is done through techniques such as balancing the training data, using more representative data, or striving to make the model's assumptions align more closely with the data's true structure.
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