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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. Generalization error - Wikipedia

    en.wikipedia.org/wiki/Generalization_error

    This is known as the bias–variance tradeoff. Keeping a function simple to avoid overfitting may introduce a bias in the resulting predictions, while allowing it to be more complex leads to overfitting and a higher variance in the predictions. It is impossible to minimize both simultaneously.

  4. Bland–Altman plot - Wikipedia

    en.wikipedia.org/wiki/Bland–Altman_plot

    A Bland–Altman plot (difference plot) in analytical chemistry or biomedicine is a method of data plotting used in analyzing the agreement between two different assays. It is identical to a Tukey mean-difference plot, [1] the name by which it is known in other fields, but was popularised in medical statistics by J. Martin Bland and Douglas G ...

  5. 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.

  6. Experimental uncertainty analysis - Wikipedia

    en.wikipedia.org/wiki/Experimental_uncertainty...

    The bias is a fixed, constant value; random variation is just that – random, unpredictable. Random variations are not predictable but they do tend to follow some rules, and those rules are usually summarized by a mathematical construct called a probability density function (PDF).

  7. Ridge regression - Wikipedia

    en.wikipedia.org/wiki/Ridge_regression

    Ridge regression is a method of estimating the coefficients of multiple-regression models in scenarios where the independent variables are highly correlated. [1] It has been used in many fields including econometrics, chemistry, and engineering. [2]

  8. 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).

  9. Evolutionary tradeoff - Wikipedia

    en.wikipedia.org/wiki/Evolutionary_tradeoff

    In evolutionary biology, an evolutionary tradeoff is a situation in which evolution cannot advance one part of a biological system without distressing another part of it. In this context, tradeoffs refer to the process through which a trait increases in fitness at the expense of decreased fitness in another trait.