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  2. Bias–variance tradeoff - Wikipedia

    en.wikipedia.org/wiki/Biasvariance_tradeoff

    Even though the biasvariance 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 ...

  3. Mean squared prediction error - Wikipedia

    en.wikipedia.org/wiki/Mean_squared_prediction_error

    The MSPE can be decomposed into two terms: the squared bias ... Bias-variance tradeoff; Mean squared error; Errors and residuals in statistics; Law of total variance;

  4. Generalization error - Wikipedia

    en.wikipedia.org/wiki/Generalization_error

    This is known as the biasvariance 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.

  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. Overfitting - Wikipedia

    en.wikipedia.org/wiki/Overfitting

    The biasvariance tradeoff is often used to overcome overfit models. With a large set of explanatory variables that actually have no relation to the dependent variable being predicted, some variables will in general be falsely found to be statistically significant and the researcher may thus retain them in the model, thereby overfitting the ...

  7. Supervised learning - Wikipedia

    en.wikipedia.org/wiki/Supervised_learning

    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).

  8. 270 Reasons Women Choose Not To Have Children - The ...

    data.huffingtonpost.com/2015/07/choosing-childfree

    Far too often, women who choose to be childfree are asked to defend their “immature,” “selfish” lifestyles. They’re told that motherhood is the “most important job in the world” and face accusations of living “meaningless” lives.

  9. Bias - Wikipedia

    en.wikipedia.org/wiki/Bias

    Media bias is the bias or perceived bias of journalists and news producers within the mass media in the selection of events, the stories that are reported, and how they are covered. The term generally implies a pervasive or widespread bias violating the standards of journalism , rather than the perspective of an individual journalist or article ...