![]() Subjects, the images were taken at different times, varying the lighting,įacial expressions (open / closed eyes, smiling / not smiling) and facialĭetails (glasses / no glasses). There are ten different images of each of 40 distinct subjects. produces Gaussianĭata with a spherical decision boundary for binary classification. centroid-basedĬlustering or linear classification), including optional Gaussian noise. Make_circles and make_moons generate 2d binary classificationĭatasets that are challenging to certain algorithms (e.g. Make_hastie_10_2 generates a similar binary, 10-dimensional problem. Near-equal-size classes separated by concentric hyperspheres. Make_gaussian_quantiles divides a single Gaussian cluster into Per class and linear transformations of the feature space. Make_classification specialises in introducing noise by way of:Ĭorrelated, redundant and uninformative features multiple Gaussian clusters ![]() Standard deviations of each cluster, and is used to demonstrate clustering. make_blobs provides greater control regarding the centers and Both make_blobs and make_classification create multiclassĭatasets by allocating each class one or more normally-distributed clusters of ![]()
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