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  2. Overfitting - Wikipedia

    en.wikipedia.org/wiki/Overfitting

    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).

  3. Bias–variance tradeoff - Wikipedia

    en.wikipedia.org/wiki/Bias–variance_tradeoff

    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 ...

  4. Regularization (mathematics) - Wikipedia

    en.wikipedia.org/wiki/Regularization_(mathematics)

    Techniques like early stopping, L1 and L2 regularization, and dropout are designed to prevent overfitting and underfitting, thereby enhancing the model's ability to adapt to and perform well with new data, thus improving model generalization. [4]

  5. Generalization error - Wikipedia

    en.wikipedia.org/wiki/Generalization_error

    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.

  6. Supervised learning - Wikipedia

    en.wikipedia.org/wiki/Supervised_learning

    When contains many candidate functions or the training set is not sufficiently large, empirical risk minimization leads to high variance and poor generalization. The learning algorithm is able to memorize the training examples without generalizing well. This is called overfitting.

  7. Decision tree pruning - Wikipedia

    en.wikipedia.org/wiki/Decision_tree_pruning

    Pruning reduces the complexity of the final classifier, and hence improves predictive accuracy by the reduction of overfitting. One of the questions that arises in a decision tree algorithm is the optimal size of the final tree. A tree that is too large risks overfitting the training data and poorly generalizing to new samples. A small tree ...

  8. Early stopping - Wikipedia

    en.wikipedia.org/wiki/Early_stopping

    The form the population iteration, which converges to , but cannot be used in computation, while the form the sample iteration which usually converges to an overfitting solution. We want to control the difference between the expected risk of the sample iteration and the minimum expected risk, that is, the expected risk of the regression function:

  9. Generalized additive model - Wikipedia

    en.wikipedia.org/wiki/Generalized_additive_model

    Overfitting can be a problem with GAMs, [22] especially if there is un-modelled residual auto-correlation or un-modelled overdispersion. Cross-validation can be used to detect and/or reduce overfitting problems with GAMs (or other statistical methods), [30] and software often allows the level of penalization to be increased to force smoother ...