Bias and Variance

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Summary: * Overfitting occurs when a statistical model describes random error or noise instead of the underlying relationship. Overfitting generally occurs . . .

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> * Variance is about the stability of a model in response to new training examples. An algorithm like K-nearest neighbours has low bias (because it doesn’t really assume anything special about the distribution of the data points) but high variance, because it can easily change its prediction in response to the composition of the training set.