Information om | Engelska ordet OVERFITTING

Exempel på hur man kan använda OVERFITTING i en mening

• For example, a model might be selected by maximizing its performance on some set of training data, and yet its suitability might be determined by its ability to perform well on unseen data; overfitting occurs when a model begins to "memorize" training data rather than "learning" to generalize from a trend.
• In machine learning, early stopping is a form of regularization used to avoid overfitting when training a model with an iterative method, such as gradient descent.
• Multi-task learning works because regularization induced by requiring an algorithm to perform well on a related task can be superior to regularization that prevents overfitting by penalizing all complexity uniformly.
• This observation that a more complex classifier (a larger forest) gets more accurate nearly monotonically is in sharp contrast to the common belief that the complexity of a classifier can only grow to a certain level of accuracy before being hurt by overfitting.
• 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.
• The advantages of these partition patterns as likely models for behavioral data are that they are describable by a minimal number of parameters, hence avoid overfitting; and that they are generalizable to partition in spaces of higher dimensionalities.
• Ridge regression improves prediction error by shrinking the sum of the squares of the regression coefficients to be less than a fixed value in order to reduce overfitting, but it does not perform covariate selection and therefore does not help to make the model more interpretable.
• Leinweber thus illustrated, tongue in cheek, how indiscriminate data mining, overfitting, and even apophenia may affect market predictions.
• This traditional geometric interpretation of SVMs provides useful intuition about how SVMs work, but is difficult to relate to other machine-learning techniques for avoiding overfitting, like regularization, early stopping, sparsity and Bayesian inference.
• Another problem with the kernel perceptron is that it does not regularize, making it vulnerable to overfitting.
• The resulting estimates generally have lower mean squared error than the OLS estimates, particularly when multicollinearity is present or when overfitting is a problem.
• Manifold regularization is a type of regularization, a family of techniques that reduces overfitting and ensures that a problem is well-posed by penalizing complex solutions.
• Studies that successfully used RLHF for this goal have noted that the use of KL regularization in RLHF, which aims to prevent the learned policy from straying too far from the unaligned model, helped to stabilize the training process by reducing overfitting to the reward model.
• Learning is constrained to a linear SVM to mitigate the risk of overfitting, and the directionality of features is enforced.

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