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  2. Feature scaling - Wikipedia

    en.wikipedia.org/wiki/Feature_scaling

    Also known as min-max scaling or min-max normalization, rescaling is the simplest method and consists in rescaling the range of features to scale the range in [0, 1] or [−1, 1]. Selecting the target range depends on the nature of the data. The general formula for a min-max of [0, 1] is given as: [3]

  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. MediaFire - Wikipedia

    en.wikipedia.org/wiki/MediaFire

    MediaFire is a file hosting, file synchronization, and cloud storage service based in Shenandoah, Texas, United States.Founded in June 2006 by Derek Labian and Tom Langridge, the company provides client software for Microsoft Windows, macOS, Linux, Android, iOS, BlackBerry 10, and web browsers. [1]

  5. scikit-image - Wikipedia

    en.wikipedia.org/wiki/Scikit-image

    scikit-image (formerly scikits.image) is an open-source image processing library for the Python programming language. [2] It includes algorithms for segmentation, geometric transformations, color space manipulation, analysis, filtering, morphology, feature detection, and more. [3]

  6. Sparse PCA - Wikipedia

    en.wikipedia.org/wiki/Sparse_PCA

    Sparse principal component analysis (SPCA or sparse PCA) is a technique used in statistical analysis and, in particular, in the analysis of multivariate data sets. It extends the classic method of principal component analysis (PCA) for the reduction of dimensionality of data by introducing sparsity structures to the input variables.

  7. Principal component regression - Wikipedia

    en.wikipedia.org/wiki/Principal_component_regression

    PCR is a form of reduced rank regression. [1] More specifically, PCR is used for estimating the unknown regression coefficients in a standard linear regression model. In PCR, instead of regressing the dependent variable on the explanatory variables directly, the principal components of the explanatory variables are used as regressors.

  8. File:Scikit learn logo small.svg - Wikipedia

    en.wikipedia.org/wiki/File:Scikit_learn_logo...

    In no event shall The scikit-learn developers be liable for any direct, indirect, incidental, special, exemplary, or consequential damages (including, but not limited to, procurement of substitute goods or services; loss of use, data, or profits; or business interruption) however caused and on any theory of liability, whether in contract ...