Answer / Chandika Prasad Upadhyay

Bias and variance are two key factors that impact the performance of a machine learning model. Bias refers to the error introduced by approximating a complex reality with a simpler, less flexible model. High bias leads to underfitting (poor fit to the training data) and poor generalization to unseen data. Variance, on the other hand, represents the sensitivity of the model to fluctuations in the training data. High variance results in overfitting (good fit to the training data but poor generalization), as the model learns noise or irrelevant patterns instead of the underlying structure.

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