Answer / Manorma Sharma

Bias and variance are two key concepts in machine learning that describe the error of a model. Bias refers to the systematic error caused by an overly simplistic or wrong assumption about the relationship between variables, leading to underfitting or overestimation of the training data. Variance, on the other hand, is the random error caused by sensitive dependence on the training set, leading to overfitting or underestimation of the test data. A good model has low bias and low variance, allowing it to generalize well to unseen data.

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