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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. Cascading classifiers - Wikipedia

    en.wikipedia.org/wiki/Cascading_classifiers

    The term is also used in statistics to describe a model that is staged. For example, a classifier (for example k -means), takes a vector of features (decision variables) and outputs for each possible classification result the probability that the vector belongs to the class.

  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. Relief (feature selection) - Wikipedia

    en.wikipedia.org/wiki/Relief_(feature_selection)

    Relief is an algorithm developed by Kira and Rendell in 1992 that takes a filter-method approach to feature selection that is notably sensitive to feature interactions. [1] [2] It was originally designed for application to binary classification problems with discrete or numerical features. Relief calculates a feature score for each feature ...

  6. Multivariate adaptive regression spline - Wikipedia

    en.wikipedia.org/wiki/Multivariate_adaptive...

    In statistics, multivariate adaptive regression splines (MARS) is a form of regression analysis introduced by Jerome H. Friedman in 1991. [1] It is a non-parametric regression technique and can be seen as an extension of linear models that automatically models nonlinearities and interactions between variables.

  7. Affinity propagation - Wikipedia

    en.wikipedia.org/wiki/Affinity_propagation

    In statistics and data mining, affinity propagation (AP) is a clustering algorithm based on the concept of "message passing" between data points. [1] Unlike clustering algorithms such as k-means or k-medoids, affinity propagation does not require the number of clusters to be determined or estimated before running the algorithm.

  8. Mixture of experts - Wikipedia

    en.wikipedia.org/wiki/Mixture_of_experts

    Specifically, the top-1 expert is always selected, and the top-2th expert is selected with probability proportional to that experts' weight according to the gating function. Later, GLaM [36] demonstrated a language model with 1.2 trillion parameters, each MoE layer using top-2 out of 64 experts. Switch Transformers [21] use top-1 in all MoE layers.

  9. Segmented regression - Wikipedia

    en.wikipedia.org/wiki/Segmented_regression

    Yr = A 1.x + K 1 for x < BP (breakpoint) Yr = A 2.x + K 2 for x > BP (breakpoint) where: Yr is the expected (predicted) value of y for a certain value of x; A 1 and A 2 are regression coefficients (indicating the slope of the line segments); K 1 and K 2 are regression constants (indicating the intercept at the y-axis).