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  2. Machine learning - Wikipedia

    en.wikipedia.org/wiki/Machine_learning

    Machine learning (ML) is a field of study in artificial intelligence concerned with the development and study of statistical algorithms that can learn from data and generalize to unseen data, and thus perform tasks without explicit instructions. [1] Recently, artificial neural networks have been able to surpass many previous approaches in ...

  3. scikit-learn - Wikipedia

    en.wikipedia.org/wiki/Scikit-learn

    scikit-learn (formerly scikits.learn and also known as sklearn) is a free and open-source machine learning library for the Python programming language. [3] It features various classification, regression and clustering algorithms including support-vector machines, random forests, gradient boosting, k-means and DBSCAN, and is designed to interoperate with the Python numerical and scientific ...

  4. Probably approximately correct learning - Wikipedia

    en.wikipedia.org/wiki/Probably_approximately...

    e. In computational learning theory, probably approximately correct (PAC) learning is a framework for mathematical analysis of machine learning. It was proposed in 1984 by Leslie Valiant. [1] In this framework, the learner receives samples and must select a generalization function (called the hypothesis) from a certain class of possible functions.

  5. Vapnik–Chervonenkis dimension - Wikipedia

    en.wikipedia.org/wiki/Vapnik–Chervonenkis...

    Vapnik–Chervonenkis dimension. In Vapnik–Chervonenkis theory, the Vapnik–Chervonenkis (VC) dimension is a measure of the size (capacity, complexity, expressive power, richness, or flexibility) of a class of sets. The notion can be extended to classes of binary functions. It is defined as the cardinality of the largest set of points that ...

  6. Decision tree learning - Wikipedia

    en.wikipedia.org/wiki/Decision_tree_learning

    v. t. e. Decision tree learning is a supervised learning approach used in statistics, data mining and machine learning. In this formalism, a classification or regression decision tree is used as a predictive model to draw conclusions about a set of observations. Tree models where the target variable can take a discrete set of values are called ...

  7. Learning curve (machine learning) - Wikipedia

    en.wikipedia.org/wiki/Learning_curve_(machine...

    One model of a machine learning is producing a function, f(x), which given some information, x, predicts some variable, y, from training data and . It is distinct from mathematical optimization because f {\displaystyle f} should predict well for x {\displaystyle x} outside of X train {\displaystyle X_{\text{train}}} .

  8. ID3 algorithm - Wikipedia

    en.wikipedia.org/wiki/ID3_algorithm

    Values of attributes are represented by branches. In decision tree learning, ID3 (Iterative Dichotomiser 3) is an algorithm invented by Ross Quinlan [1] used to generate a decision tree from a dataset. ID3 is the precursor to the C4.5 algorithm, and is typically used in the machine learning and natural language processing domains.

  9. Minimum description length - Wikipedia

    en.wikipedia.org/wiki/Minimum_description_length

    Minimum Description Length (MDL) is a model selection principle where the shortest description of the data is the best model. MDL methods learn through a data compression perspective and are sometimes described as mathematical applications of Occam's razor. The MDL principle can be extended to other forms of inductive inference and learning ...

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