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Bias: Bias is disproportionate weight in favor of or against an idea or thing, usually in a way that is closed-minded, prejudicial, or unfair. Biases can be innate or learned.
Bias of an estimator: In statistics, the bias of an estimator is the difference between this estimator's expected value and the true value of the parameter being estimated. An estimator or decision rule with zero bias is called unbiased.
Bias–variance tradeoff: In statistics and machine learning, the bias–variance tradeoff is the property of a set of predictive models whereby models with a lower bias in parameter estimation have a higher variance of the parameter estimates across samples, and vice versa. The bias–variance dilemma or bias–variance problem is the conflict in trying to simultaneously minimize these two sources of error that prevent supervised learning algorithms from generalizing beyond their training set: The bias error is an error from erroneous assumptions in the learning algorithm.
Biasing: In electronics, biasing is the setting of initial operating conditions of an active device in an amplifier. Many electronic devices, such as diodes, transistors and vacuum tubes, whose function is processing time-varying (AC) signals, also require a steady (DC) current or voltage at their terminals to operate correctly.