擅长:python、mysql、java
<p>请参阅下面的代码和注释。在</p>
<pre><code>import numpy as np
from sklearn.datasets import make_classification
from sklearn import feature_selection
# Build a classification task using 3 informative features
X, y = make_classification(n_samples=1000,
n_features=10,
n_informative=3,
n_redundant=0,
n_repeated=0,
n_classes=2,
random_state=0,
shuffle=False)
sp = feature_selection.SelectPercentile(feature_selection.f_regression, percentile=30)
sp.fit_transform(X[:-1], y[:-1]) #here, training are the first 9 data vectors, and the last one is the test set
idx = np.arange(0, X.shape[1]) #create an index array
features_to_keep = idx[sp.get_support() == True] #get index positions of kept features
x_fs = X[:,features_to_keep] #prune X data vectors
x_test_fs = x_fs[-1] #take your last data vector (the test set) pruned values
print x_test_fs #these are your pruned test set values
</code></pre>