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Supervised and unsupervised machine learning are two types of algorithms used in AI. Supervised algorithms use labeled data, while unsupervised algorithms use unlabeled data. Semi-supervised learning is a combination of both methods. Machine learning bias is when an algorithm produces prejudiced results due to oversimplification, and variance is error due to overcomplexity in a learning algorithm. The relationship between these two terms is sometimes framed as the “bias-variance dilemma” or the “bias-variance tradeoff”.
