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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).
But if the learning algorithm is too flexible, it will fit each training data set differently, and hence have high variance. A key aspect of many supervised learning methods is that they are able to adjust this tradeoff between bias and variance (either automatically or by providing a bias/variance parameter that the user can adjust).
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.
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.
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).
The tradeoff exists because accommodating flexibility requires satisfying a larger set of requirements, which results in complexity and usability compromises. [ 1 ] Design theory maintains that over their lifecycle, systems shift from supporting multiple uses inefficiently, towards efficiently supporting a single use as users' needs become more ...
SIMALTO – SImultaneous Multi-Attribute Trade Off – is a survey based statistical technique used in market research that helps determine how people prioritise and value alternative product and/or service options of the attributes that make up individual products or services.
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