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Even though the bias–variance decomposition does not directly apply in reinforcement learning, a similar tradeoff can also characterize generalization. When an agent has limited information on its environment, the suboptimality of an RL algorithm can be decomposed into the sum of two terms: a term related to an asymptotic bias and a term due ...
This can be seen by noting the following formula, which follows from the Bienaymé formula, for the term in the inequality for the expectation of the uncorrected sample variance above: [(¯)] =. In other words, the expected value of the uncorrected sample variance does not equal the population variance σ 2 , unless multiplied by a ...
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.
Therefore, manipulating corresponds to trading-off bias and variance. For problems with high-variance w {\displaystyle w} estimates, such as cases with relatively small n {\displaystyle n} or with correlated regressors, the optimal prediction accuracy may be obtained by using a nonzero λ {\displaystyle \lambda } , and thus introducing some ...
In any network, the bias can be reduced at the cost of increased variance; In a group of networks, the variance can be reduced at no cost to the bias. This is known as the bias–variance tradeoff. Ensemble averaging creates a group of networks, each with low bias and high variance, and combines them to form a new network which should ...
Given an r-sample statistic, one can create an n-sample statistic by something similar to bootstrapping (taking the average of the statistic over all subsamples of size r). This procedure is known to have certain good properties and the result is a U-statistic. The sample mean and sample variance are of this form, for r = 1 and r = 2.
Download as PDF; Printable version; In other projects Wikidata item; ... Bias–variance tradeoff; Model selection; Cross-validation (statistics) Validity (statistics)
This could be appropriate for example when errors in y and x are both caused by measurements, and the accuracy of measuring devices or procedures are known. The case when δ = 1 is also known as the orthogonal regression. Regression with known reliability ratio λ = σ² ∗ / ( σ² η + σ² ∗), where σ² ∗ is the variance of the latent ...