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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. Artificial intelligence - Wikipedia

    en.wikipedia.org/wiki/Artificial_intelligence

    Artificial intelligence (AI), in its broadest sense, is intelligence exhibited by machines, particularly computer systems.It is a field of research in computer science that develops and studies methods and software that enable machines to perceive their environment and use learning and intelligence to take actions that maximize their chances of achieving defined goals. [1]

  4. Generalization error - Wikipedia

    en.wikipedia.org/wiki/Generalization_error

    Overfitting occurs when the learned function becomes sensitive to the noise in the sample. As a result, the function will perform well on the training set but not perform well on other data from the joint probability distribution of x {\displaystyle x} and y {\displaystyle y} .

  5. Ensemble learning - Wikipedia

    en.wikipedia.org/wiki/Ensemble_learning

    To reduce overfitting, a member can be validated using the out-of-bag set (the examples that are not in its bootstrap set). [21] Inference is done by voting of predictions of ensemble members, called aggregation. It is illustrated below with an ensemble of four decision trees. The query example is classified by each tree.

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

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

    A training data set is a data set of examples used during the learning process and is used to fit the parameters (e.g., weights) of, for example, a classifier. [9] [10]For classification tasks, a supervised learning algorithm looks at the training data set to determine, or learn, the optimal combinations of variables that will generate a good predictive model. [11]

  7. 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 ...

  8. Who is Shaboozey, the 'Tipsy' singer up for best new artist ...

    www.aol.com/shaboozey-tipsy-singer-best-artist...

    "A Bar Song (Tipsy)" is also up for best remixed recording, but that award would go to the remixer, David Guetta. Even outside the handful of nominations, the tune has made history on many fronts. ...

  9. Double descent - Wikipedia

    en.wikipedia.org/wiki/Double_descent

    Glossary of artificial intelligence; ... This phenomenon has been considered surprising, as it contradicts assumptions about overfitting in classical machine learning ...