7.3. Generated datasets#
In addition, scikitlearn includes various random sample generators that can be used to build artificial datasets of controlled size and complexity.
7.3.1. Generators for classification and clustering#
These generators produce a matrix of features and corresponding discrete targets.
7.3.1.1. Single label#
Both make_blobs
and make_classification
create multiclass
datasets by allocating each class one or more normallydistributed 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
specializes 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
nearequalsize classes separated by concentric hyperspheres.
make_hastie_10_2
generates a similar binary, 10dimensional problem.
make_circles
and make_moons
generate 2d binary classification
datasets that are challenging to certain algorithms (e.g. centroidbased
clustering or linear classification), including optional Gaussian noise.
They are useful for visualization. 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 bagofwords mixtures include:
Pertopic 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#

Generate a constant block diagonal structure array for biclustering. 

Generate an array with block checkerboard structure for biclustering. 
7.3.2. Generators for regression#
make_regression
produces regression targets as an optionallysparse
random linear combination of random features, with noise. Its informative
features may be uncorrelated, or low rank (few features account for most of the
variance).
Other regression generators generate functions deterministically from
randomized features. make_sparse_uncorrelated
produces a target as a
linear combination of four features with fixed coefficients.
Others encode explicitly nonlinear relations:
make_friedman1
is related by polynomial and sine transforms;
make_friedman2
includes feature multiplication and reciprocation; and
make_friedman3
is similar with an arctan transformation on the target.
7.3.3. Generators for manifold learning#

Generate an S curve dataset. 

Generate a swiss roll dataset. 
7.3.4. Generators for decomposition#

Generate a mostly low rank matrix with bellshaped singular values. 

Generate a signal as a sparse combination of dictionary elements. 

Generate a random symmetric, positivedefinite matrix. 

Generate a sparse symmetric definite positive matrix. 