A classification example using Linear Discriminant Analysis (LDA).
Source code: lda.py
import numpy as np
import pylab as pl
################################################################################
# import some data to play with
# The IRIS dataset
from scikits.learn import datasets, svm
iris = datasets.load_iris()
# Some noisy data not correlated
E = np.random.normal(size=(len(iris.data), 35))
# Add the noisy data to the informative features
X = np.hstack((iris.data, E))
y = iris.target
################################################################################
# LDA
from scikits.learn.lda import LDA
lda = LDA()
y_pred = lda.fit(X, y).predict(X)
print "Number of mislabeled points : %d"%(y != y_pred).sum()