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  2. t-distributed stochastic neighbor embedding - Wikipedia

    en.wikipedia.org/wiki/T-distributed_stochastic...

    ELKI contains tSNE, also with Barnes-Hut approximation; scikit-learn, a popular machine learning library in Python implements t-SNE with both exact solutions and the Barnes-Hut approximation. Tensorboard, the visualization kit associated with TensorFlow, also implements t-SNE (online version) The Julia package TSne implements t-SNE

  3. Taylor diagram - Wikipedia

    en.wikipedia.org/wiki/Taylor_diagram

    The bias, like the standard deviation, should also be normalized in order to plot multiple parameters on a single diagram. Furthermore, the mean square difference between a model and the data can be calculated by adding in quadrature the bias and the standard deviation of the errors.

  4. Self-organizing map - Wikipedia

    en.wikipedia.org/wiki/Self-organizing_map

    The second plot shows average distance to neighbours: larger distances are darker. The third plot predicts Republican (red) or Democratic (blue) party membership. The other plots each overlay the resulting map with predicted values on an input dimension: red means a predicted 'yes' vote on that bill, blue means a 'no' vote.

  5. Astropy - Wikipedia

    en.wikipedia.org/wiki/Astropy

    Astropy is a collection of software packages written in the Python programming language and designed for use in astronomy. [2] The software is a single, free, core package for astronomical utilities due to the increasingly widespread usage of Python by astronomers, and to foster interoperability between various extant Python astronomy packages. [3]

  6. Sina plot - Wikipedia

    en.wikipedia.org/wiki/Sina_plot

    A sina plot is a type of diagram in which numerical data are depicted by points distributed in such a way that the width of the point distribution is proportional to the kernel density. [ 1 ] [ 2 ] Sina plots are similar to violin plots , but while violin plots depict kernel density, sina plots depict the points themselves.

  7. Nonlinear dimensionality reduction - Wikipedia

    en.wikipedia.org/wiki/Nonlinear_dimensionality...

    Plot of the two-dimensional points that results from using a NLDR algorithm. In this case, Manifold Sculpting is used to reduce the data into just two dimensions (rotation and scale). The reduced-dimensional representations of data are often referred to as "intrinsic variables".

  8. Cophenetic correlation - Wikipedia

    en.wikipedia.org/wiki/Cophenetic_correlation

    In statistics, and especially in biostatistics, cophenetic correlation [1] (more precisely, the cophenetic correlation coefficient) is a measure of how faithfully a dendrogram preserves the pairwise distances between the original unmodeled data points.

  9. Phase plane - Wikipedia

    en.wikipedia.org/wiki/Phase_plane

    In applied mathematics, in particular the context of nonlinear system analysis, a phase plane is a visual display of certain characteristics of certain kinds of differential equations; a coordinate plane with axes being the values of the two state variables, say (x, y), or (q, p) etc. (any pair of variables).