50 lines
1.6 KiB
Python
50 lines
1.6 KiB
Python
"""
|
|
==============================================
|
|
Plot randomly generated classification dataset
|
|
==============================================
|
|
|
|
Plot several randomly generated 2D classification datasets.
|
|
This example illustrates the `datasets.make_classification`
|
|
function.
|
|
|
|
Three binary and two multi-class classification datasets
|
|
are generated, with different numbers of informative
|
|
features and clusters per class.
|
|
"""
|
|
|
|
print(__doc__)
|
|
|
|
import pylab as pl
|
|
|
|
from sklearn.datasets import make_classification
|
|
|
|
pl.figure(figsize=(8, 6))
|
|
pl.subplots_adjust(bottom=.05, top=.9, left=.05, right=.95)
|
|
|
|
pl.subplot(221)
|
|
pl.title("One informative feature, one cluster", fontsize='small')
|
|
X1, Y1 = make_classification(n_features=2, n_redundant=0, n_informative=1,
|
|
n_clusters_per_class=1)
|
|
pl.scatter(X1[:, 0], X1[:, 1], marker='o', c=Y1)
|
|
|
|
pl.subplot(222)
|
|
pl.title("Two informative features, one cluster", fontsize='small')
|
|
X1, Y1 = make_classification(n_features=2, n_redundant=0, n_informative=2,
|
|
n_clusters_per_class=1)
|
|
pl.scatter(X1[:, 0], X1[:, 1], marker='o', c=Y1)
|
|
|
|
pl.subplot(223)
|
|
pl.title("Two informative features, two clusters", fontsize='small')
|
|
X2, Y2 = make_classification(n_features=2, n_redundant=0, n_informative=2)
|
|
pl.scatter(X2[:, 0], X2[:, 1], marker='o', c=Y2)
|
|
|
|
|
|
pl.subplot(224)
|
|
pl.title("Multi-class, two informative features, one cluster",
|
|
fontsize='small')
|
|
X1, Y1 = make_classification(n_features=2, n_redundant=0, n_informative=2,
|
|
n_clusters_per_class=1, n_classes=3)
|
|
pl.scatter(X1[:, 0], X1[:, 1], marker='o', c=Y1)
|
|
|
|
pl.show()
|