7.3. Generated datasets¶

7.3.1.1. Single label¶

Both make_blobs and make_classification create multiclass datasets by allocating each class one or more normally-distributed clusters of points. make_blobs provides greater control regarding the centers and standard deviations of each cluster, and is used to demonstrate clustering. make_classification specialises in introducing noise by way of: correlated, redundant and uninformative features; multiple Gaussian clusters per class; and linear transformations of the feature space.

make_gaussian_quantiles divides a single Gaussian cluster into near-equal-size classes separated by concentric hyperspheres. make_hastie_10_2 generates a similar binary, 10-dimensional problem.

make_circles and make_moons generate 2d binary classification datasets that are challenging to certain algorithms (e.g. centroid-based clustering or linear classification), including optional Gaussian noise. They are useful for visualisation. make_circles produces Gaussian data with a spherical decision boundary for binary classification, while make_moons produces two interleaving half circles.

7.3.1.2. Multilabel¶

make_multilabel_classification generates random samples with multiple labels, reflecting a bag of words drawn from a mixture of topics. The number of topics for each document is drawn from a Poisson distribution, and the topics themselves are drawn from a fixed random distribution. Similarly, the number of words is drawn from Poisson, with words drawn from a multinomial, where each topic defines a probability distribution over words. Simplifications with respect to true bag-of-words mixtures include:

• Per-topic word distributions are independently drawn, where in reality all would be affected by a sparse base distribution, and would be correlated.

• For a document generated from multiple topics, all topics are weighted equally in generating its bag of words.

• Documents without labels words at random, rather than from a base distribution.

7.3.1.3. Biclustering¶