<ul><li>In machine learning, bias refers to systematic errors that occur when models fail to capture the true underlying relationships within the data.</li><li>Quantifying bias requires techniques like the bias-variance tradeoff, cross-validation, and learning curves.</li><li>Addressing bias involves data preprocessing, feature engineering, model selection, and considering algorithmic fairness.</li><li>By understanding and quantifying bias, machine learning practitioners can build more reliable and equitable models.</li></ul>

Strategies for quantifying and mitigating biases in Machine Learning

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