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Please cite us if you use the software. Generators for classification and clustering 5. The 20 newsgroups text dataset 5. The Labeled Faces in the Wild face recognition dataset 5. Optical Recognition of Handwritten Digits Data Set 5. There are three distinct kinds of dataset interfaces for different types of datasets. See the dataset descriptions below for details.
These datasets are useful to quickly illustrate the behavior of the various algorithms implemented in the scikit. They are however often too small to be representative of real world machine learning tasks. The scikit also embed a couple of sample JPEG images published under Creative Commons license by their authors. Those image can be useful to test algorithms and pipeline on 2D data. Load sample images for image manipulation. Often machine learning algorithms work best if the input is converted to a floating point representation first.
Also, if you plan to use matplotlib. 0 – 1 as done in the following example. In addition, scikit-learn includes various random sample generators that can be used to build artificial datasets of controlled size and complexity. These generators produce a matrix of features and corresponding discrete targets. Gaussian cluster into near-equal-size classes separated by concentric hyperspheres.