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  2. Oversampling and undersampling in data analysis - Wikipedia

    en.wikipedia.org/wiki/Oversampling_and_under...

    Data augmentation in data analysis are techniques used to increase the amount of data by adding slightly modified copies of already existing data or newly created synthetic data from existing data. It acts as a regularizer and helps reduce overfitting when training a machine learning model. [8] (See: Data augmentation)

  3. Overfitting - Wikipedia

    en.wikipedia.org/wiki/Overfitting

    In mathematical modeling, overfitting is "the production of an analysis that corresponds too closely or exactly to a particular set of data, and may therefore fail to fit to additional data or predict future observations reliably". [1] An overfitted model is a mathematical model that contains more parameters than can be justified by the data. [2]

  4. 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.

  5. Regularization (mathematics) - Wikipedia

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

    In machine learning, a key challenge is enabling models to accurately predict outcomes on unseen data, not just on familiar training data.Regularization is crucial for addressing overfitting—where a model memorizes training data details but can't generalize to new data.

  6. 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.

  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. Ashton Jeanty Heisman Trophy odds: Stats, more for Boise ...

    www.aol.com/ashton-jeanty-heisman-trophy-odds...

    Here's the latest on Ashton Jeanty's Heisman Trophy odds after leading No. 13 BYU to a 9-1 record to start the 2024 college football season:

  9. Double descent - Wikipedia

    en.wikipedia.org/wiki/Double_descent

    A model of double descent at the thermodynamic limit has been analyzed using the replica trick, and the result has been confirmed numerically. [ 11 ] Empirical examples