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  2. 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:

  3. Generalization error - Wikipedia

    en.wikipedia.org/wiki/Generalization_error

    Data points were generated from the relationship y = x with white noise added to the y values. In the left column, a set of training points is shown in blue. A seventh order polynomial function was fit to the training data. In the right column, the function is tested on data sampled from the underlying joint probability distribution of x and y ...

  4. CatBoost - Wikipedia

    en.wikipedia.org/wiki/Catboost

    CatBoost [6] is an open-source software library developed by Yandex.It provides a gradient boosting framework which, among other features, attempts to solve for categorical features using a permutation-driven alternative to the classical algorithm. [7]

  5. Dilution (neural networks) - Wikipedia

    en.wikipedia.org/wiki/Dilution_(neural_networks)

    On the left is a fully connected neural network with two hidden layers. On the right is the same network after applying dropout. Dilution and dropout (also called DropConnect [1]) are regularization techniques for reducing overfitting in artificial neural networks by preventing complex co-adaptations on training data.

  6. Overfitting - Wikipedia

    en.wikipedia.org/wiki/Overfitting

    Regularization: Regularization is a technique used to prevent overfitting by adding a penalty term to the loss function that discourages large parameter values. It can also be used to prevent underfitting by controlling the complexity of the model. [15] Ensemble Methods: Ensemble methods combine multiple models to create a more accurate ...

  7. Training, validation, and test data sets - Wikipedia

    en.wikipedia.org/wiki/Training,_validation,_and...

    A test data set is a data set that is independent of the training data set, but that follows the same probability distribution as the training data set. If a model fit to the training data set also fits the test data set well, minimal overfitting has taken place (see figure below). A better fitting of the training data set as opposed to the ...

  8. The 6 most common headache types — and when to see a doctor

    www.aol.com/6-most-common-headache-types...

    Certain types of headaches may be a sign of a more serious condition, such as a brain tumor or aneurysm, especially if the pain is sudden or severe, according to Cohen. "This highlights the ...

  9. 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 ...