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  2. Bias–variance tradeoff - Wikipedia

    en.wikipedia.org/wiki/Bias–variance_tradeoff

    In artificial neural networks, the variance increases and the bias decreases as the number of hidden units increase, [12] although this classical assumption has been the subject of recent debate. [4] Like in GLMs, regularization is typically applied. In k-nearest neighbor models, a high value of k leads to high bias and low variance (see below).

  3. Trade-off - Wikipedia

    en.wikipedia.org/wiki/Trade-off

    In economics a trade-off is expressed in terms of the opportunity cost of a particular choice, which is the loss of the most preferred alternative given up. [2] A tradeoff, then, involves a sacrifice that must be made to obtain a certain product, service, or experience, rather than others that could be made or obtained using the same required resources.

  4. Williamson tradeoff model - Wikipedia

    en.wikipedia.org/wiki/Williamson_tradeoff_model

    The Williamson tradeoff model is a theoretical model in the economics of industrial organization which emphasizes the tradeoff associated with horizontal mergers between gains resulting from lower costs of production and the losses associated with higher prices due to greater degree of monopoly power. [1]

  5. Supervised learning - Wikipedia

    en.wikipedia.org/wiki/Supervised_learning

    A first issue is the tradeoff between bias and variance. [2] Imagine that we have available several different, but equally good, training data sets. A learning algorithm is biased for a particular input x {\displaystyle x} if, when trained on each of these data sets, it is systematically incorrect when predicting the correct output for x ...

  6. Bias of an estimator - Wikipedia

    en.wikipedia.org/wiki/Bias_of_an_estimator

    In statistics, the bias of an estimator (or bias function) 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. In statistics, "bias" is an objective property of an estimator.

  7. Overfitting - Wikipedia

    en.wikipedia.org/wiki/Overfitting

    Underfitting is the inverse of overfitting, meaning that the statistical model or machine learning algorithm is too simplistic to accurately capture the patterns in the data. A sign of underfitting is that there is a high bias and low variance detected in the current model or algorithm used (the inverse of overfitting: low bias and high variance).

  8. Risk–return spectrum - Wikipedia

    en.wikipedia.org/wiki/Risk–return_spectrum

    The term structure of the risk-return tradeoff. No. w11119. National Bureau of Economic Research, 2005. Lundblad, Christian. "The risk return tradeoff in the long run: 1836–2003." Journal of Financial Economics 85.1 (2007): 123-150. Lettau, Martin, and Sydney Ludvigson. "Measuring and modeling variation in the risk-return tradeoff."

  9. Trade-off theory of capital structure - Wikipedia

    en.wikipedia.org/wiki/Trade-Off_Theory_of...

    As the debt equity ratio (i.e. leverage) increases, there is a trade-off between the interest tax shield and bankruptcy, causing an optimum capital structure, D/E*.The top curve shows the tax shield gains of debt financing, while the bottom curve includes that minus the costs of bankruptcy.