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  2. 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 ...

  3. Support vector machine - Wikipedia

    en.wikipedia.org/wiki/Support_vector_machine

    Analogously, the model produced by SVR depends only on a subset of the training data, because the cost function for building the model ignores any training data close to the model prediction. Another SVM version known as least-squares support vector machine (LS-SVM) has been proposed by Suykens and Vandewalle.

  4. scikit-multiflow - Wikipedia

    en.wikipedia.org/wiki/Scikit-multiflow

    The scikit-multiflow library is implemented under the open research principles and is currently distributed under the BSD 3-clause license. scikit-multiflow is mainly written in Python, and some core elements are written in Cython for performance. scikit-multiflow integrates with other Python libraries such as Matplotlib for plotting, scikit-learn for incremental learning methods [4 ...

  5. Platt scaling - Wikipedia

    en.wikipedia.org/wiki/Platt_scaling

    In machine learning, Platt scaling or Platt calibration is a way of transforming the outputs of a classification model into a probability distribution over classes.The method was invented by John Platt in the context of support vector machines, [1] replacing an earlier method by Vapnik, but can be applied to other classification models. [2]

  6. Least-angle regression - Wikipedia

    en.wikipedia.org/wiki/Least-angle_regression

    Standardized coefficients shown as a function of proportion of shrinkage. In statistics, least-angle regression (LARS) is an algorithm for fitting linear regression models to high-dimensional data, developed by Bradley Efron, Trevor Hastie, Iain Johnstone and Robert Tibshirani.

  7. Conditional logistic regression - Wikipedia

    en.wikipedia.org/wiki/Conditional_logistic...

    If we hold the number of strata fixed and increase the amount of data, estimates of the model parameters (for each stratum and the vector ) converge to their true values. Pathological behavior, however, occurs when we have many small strata because the number of parameters grow with the amount of data.

  8. Mixture of experts - Wikipedia

    en.wikipedia.org/wiki/Mixture_of_experts

    In mixture of softmaxes, the model outputs multiple vectors ,, …,,, and predict the next word as = (,), where is a probability distribution by a linear-softmax operation on the activations of the hidden neurons within the model. The original paper demonstrated its effectiveness for recurrent neural networks. This was later found to work for ...

  9. Boosting (machine learning) - Wikipedia

    en.wikipedia.org/wiki/Boosting_(machine_learning)

    scikit-learn, an open source machine learning library for Python; Orange, a free data mining software suite, module Orange.ensemble; Weka is a machine learning set of tools that offers variate implementations of boosting algorithms like AdaBoost and LogitBoost