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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
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.
More abstractly, learning curves plot the difference between learning effort and predictive performance, where "learning effort" usually means the number of training samples, and "predictive performance" means accuracy on testing samples. [3] Learning curves have many useful purposes in ML, including: [4] [5] [6] choosing model parameters ...
Matplotlib (portmanteau of MATLAB, plot, and library [3]) is a plotting library for the Python programming language and its numerical mathematics extension NumPy.It provides an object-oriented API for embedding plots into applications using general-purpose GUI toolkits like Tkinter, wxPython, Qt, or GTK.
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.
Mosaic plot showing cross-sectional distribution through time of different musical themes in the Guardian's list of "1000 songs to hear before you die". A mosaic plot , Marimekko chart , Mekko chart , or sometimes percent stacked bar plot , is a graphical visualization of data from two or more qualitative variables. [ 1 ]
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.
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".