For supervised learning applications in machine learning and statistical learning theory, generalization error[1] (also known as the out-of-sample error[2] or the risk) is a measure of how accurately an algorithm is able to predict outcome values for previously unseen data. Because learning algorithms are evaluated on finite samples, the evaluation of a learning algorithm may be sensitive to sampling error. As a result, measurements of prediction error on the current data may not provide much information about predictive ability on new data. Generalization error can be minimized by avoiding overfitting in the learning algorithm. The performance of a machine learning algorithm is visualized by plots that show values of estimates of the generalization error through the learning process, which are called learning curves.
^Mohri, M., Rostamizadeh A., Talwakar A., (2018) Foundations of Machine learning, 2nd ed., Boston: MIT Press
^Y S. Abu-Mostafa, M.Magdon-Ismail, and H.-T. Lin (2012) Learning from Data, AMLBook Press. ISBN 978-1600490064
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machine learning and statistical learning theory, generalizationerror (also known as the out-of-sample error or the risk) is a measure of how accurately an...
on insufficiently-large samples under an implied margin or error. A faulty generalization often follows the following format: The proportion Q of the...
statistical quality of an algorithm is measured through the so-called generalizationerror. To solve a given problem of supervised learning, one has to perform...
subdivided into categories such as improper presumption, faulty generalization, error in assigning causation, and relevance, among others. The use of...
learner's fit to the training data comes at the expense of increased generalizationerror. Early stopping rules provide guidance as to how many iterations...
the generalizationerror of the classifier. These bounds are frequently shown using the VC dimension. Of particular prominence is the generalization error...
general the larger the margin, the lower the generalizationerror of the classifier. A lower generalizationerror means that the implementer is less likely...
{\displaystyle h} represents a binary classifier, the error function is a 0–1 loss function, i.e. the error function f h {\displaystyle f_{h}} returns 0 if...
of random forests, in particular: Using out-of-bag error as an estimate of the generalizationerror. Measuring variable importance through permutation...
probability (the "probably" part), the selected function will have low generalizationerror (the "approximately correct" part). The learner must be able to learn...
boundary. Decision boundary instability can be incorporated with generalizationerror as a standard for selecting the most accurate and stable classifier...
in the expansion (usually the higher terms). This affects accuracy. The error usually varies within the interval. Thus the numbers zeroth, first, second...
decomposition is one way to quantify generalizationerror. For the best performance in the context of generalization, the complexity of the hypothesis should...
thus insufficiently be able to predict future data results (see Generalizationerror). Shown in Figure 5 the linear line could not represent all the given...
describing the expected generalizationerror of high-dimensional kernel regression; these results immediately explain the generalization of sufficiently wide...
[ 0 , ∞ ) 2 {\displaystyle [0,\infty )^{2}} . This is not the only generalization of big O to multivariate functions, and in practice, there is some inconsistency...
significant figures, resulting in 12.345 kg as the accurate value. The rounding error (in this example, 0.00025 kg = 0.25 g) approximates the numerical resolution...
process for machine-learning in which the learned function has low generalization-error WHP. Gossip protocols: a communication protocol used in distributed...
approximation error in a data value is the discrepancy between an exact value and some approximation to it. This error can be expressed as an absolute error (the...
renders the argument invalid, while an informal fallacy originates in an error in reasoning other than an improper logical form. Arguments containing informal...
In science and engineering, convention dictates that unless a margin of error is explicitly stated, the number of significant figures used in the presentation...
adhere to. As a result, they are able to predict speech errors. Four generalizations about speech errors have been identified: Interacting elements tend to...
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