45 lines
1.5 KiB
Python
45 lines
1.5 KiB
Python
"""
|
|
===================================================
|
|
Recursive feature elimination with cross-validation
|
|
===================================================
|
|
|
|
A recursive feature elimination example with automatic tuning of the
|
|
number of features selected with cross-validation.
|
|
"""
|
|
print __doc__
|
|
|
|
import numpy as np
|
|
from sklearn.svm import SVC
|
|
from sklearn.cross_validation import StratifiedKFold
|
|
from sklearn.feature_selection import RFECV
|
|
from sklearn.datasets import make_classification
|
|
from sklearn.metrics import zero_one
|
|
|
|
# Build a classification task using 3 informative features
|
|
X, y = make_classification(n_samples=1000,
|
|
n_features=25,
|
|
n_informative=3,
|
|
n_redundant=2,
|
|
n_repeated=0,
|
|
n_classes=8,
|
|
n_clusters_per_class=1,
|
|
random_state=0)
|
|
|
|
# Create the RFE object and compute a cross-validated score.
|
|
svc = SVC(kernel="linear")
|
|
rfecv = RFECV(estimator=svc,
|
|
step=1,
|
|
cv=StratifiedKFold(y, 2),
|
|
loss_func=zero_one)
|
|
rfecv.fit(X, y)
|
|
|
|
print "Optimal number of features : %d" % rfecv.n_features_
|
|
|
|
# Plot number of features VS. cross-validation scores
|
|
import pylab as pl
|
|
pl.figure()
|
|
pl.xlabel("Number of features selected")
|
|
pl.ylabel("Cross validation score (nb of misclassifications)")
|
|
pl.plot(xrange(1, len(rfecv.cv_scores_) + 1), rfecv.cv_scores_)
|
|
pl.show()
|