machine learning and statistical learning theory, generalization error (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 of error. A faulty generalization often follows the following format: The proportion Q of the...

to bound the generalization error of these classifiers. These bounds are frequently shown using the VC dimension. The generalization error bound in boosting...

MacReady, William G. (1999). "An Efficient Method to Estimate Bagging's Generalization Error" (PDF). Machine Learning. 35 (1): 41–55. doi:10.1023/A:1007519102914...

bias). This statistical quality of an algorithm is measured via a generalization error. To solve a given problem of supervised learning, the following steps...

model's fit to the training data comes at the expense of increased generalization error. Early stopping rules provide guidance as to how many iterations...

expected generalization error with respect to a particular problem as a sum of three terms, the bias, variance, and a quantity called the irreducible error, resulting...

subdivided into categories such as improper presumption, faulty generalization, error in assigning causation, and relevance, among others. The use of...

general the larger the margin, the lower the generalization error of the classifier. A lower generalization error means that the implementer is less likely...

probability (the "probably" part), the selected function will have low generalization error (the "approximately correct" part). The learner must be able to learn...

modifications to the algorithm. It is always intended to reduce the generalization error, i.e. the error score with the trained model on the evaluation set (testing...

; Sompolinsky, Haim (2020-12-01). "High-dimensional dynamics of generalization error in neural networks". Neural Networks. 132: 428–446. doi:10.1016/j...

Mishra, Siddhartha; Molinaro, Roberto (2021-04-01). "Estimates on the generalization error of Physics Informed Neural Networks (PINNs) for approximating a class...

describing the expected generalization error of high-dimensional kernel regression; these results immediately explain the generalization of sufficiently wide...

some terms of the series used in the expansion. This affects accuracy. The error usually varies within the interval. Thus the terms (zeroth, first, second...

selection of the best set of hyperparameters and for error estimation (and assessment of generalization capacity), a nested cross-validation is required....

that the true error of a classifier is not much higher than its estimated error, and so selecting a classifier that has low estimated error will ensure...

thus insufficiently be able to predict future data results (see Generalization error). As shown in Figure 5, the linear line could not represent all the...

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...

of random forests, in particular: Using out-of-bag error as an estimate of the generalization error. Measuring variable importance through permutation...

classification or regression. As random forests give an internal estimate of generalization error, cross-validation is unnecessary. In addition, they produce proximities...

presence of over-training and to select hyperparameters to minimize the generalization error. The second is to use some form of regularization. This concept emerges...

boundary. Decision boundary instability can be incorporated with generalization error as a standard for selecting the most accurate and stable classifier...

information bottleneck is one way to control generalization error in deep learning. Namely, the generalization error is proven to scale as O ~ ( I ( X , T )...

The approximation error in a given data value represents the significant discrepancy that arises when an exact, true value is compared against some approximation...

stability of a learning algorithm and showed that it implies low generalization error. Uniform hypothesis stability, however, is a strong condition that...

the generalization error of learning functions from K {\displaystyle K} , relative to the squared loss:: 61  Prob ⁡ [ sup h ∈ K | GeneralizationError (...

[ 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...

the current model. Expected error reduction: label those points that would most reduce the model's generalization error. Exponentiated Gradient Exploration...

processing, a minimum mean square error (MMSE) estimator is an estimation method which minimizes the mean square error (MSE), which is a common measure...