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  2. Early stopping - Wikipedia

    en.wikipedia.org/wiki/Early_stopping

    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. Such methods update the model to make it better fit the training data with each iteration. Up to a point, this improves the model's performance on data outside of the training set (e ...

  3. 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 ...

  4. Decision tree pruning - Wikipedia

    en.wikipedia.org/wiki/Decision_tree_pruning

    Pre-pruning procedures prevent a complete induction of the training set by replacing a stop criterion in the induction algorithm (e.g. max. Tree depth or information gain (Attr)> minGain). Pre-pruning methods are considered to be more efficient because they do not induce an entire set, but rather trees remain small from the start.

  5. Regularization (mathematics) - Wikipedia

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

    It is often used in solving ill-posed problems or to prevent overfitting. [2] Although regularization procedures can be divided in many ways, the following delineation is particularly helpful: Explicit regularization is regularization whenever one explicitly adds a term to the optimization problem. These terms could be priors, penalties, or ...

  6. 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.

  7. Regularization perspectives on support vector machines

    en.wikipedia.org/wiki/Regularization...

    This provides a theoretical framework with which to analyze SVM algorithms and compare them to other algorithms with the same goals: to generalize without overfitting. SVM was first proposed in 1995 by Corinna Cortes and Vladimir Vapnik , and framed geometrically as a method for finding hyperplanes that can separate multidimensional data into ...

  8. Decision tree learning - Wikipedia

    en.wikipedia.org/wiki/Decision_tree_learning

    Decision-tree learners can create over-complex trees that do not generalize well from the training data. (This is known as overfitting. [37]) Mechanisms such as pruning are necessary to avoid this problem (with the exception of some algorithms such as the Conditional Inference approach, that does not require pruning). [21] [22]

  9. Data augmentation - Wikipedia

    en.wikipedia.org/wiki/Data_augmentation

    Data augmentation is a statistical technique which allows maximum likelihood estimation from incomplete data. [1] [2] Data augmentation has important applications in Bayesian analysis, [3] and the technique is widely used in machine learning to reduce overfitting when training machine learning models, [4] achieved by training models on several slightly-modified copies of existing data.