python
import numpy as np
from mvpa2.suite import *
n_samples = 100
n_features = 10
X = np.random.randn(n_samples, n_features)
y = np.random.randint(0, 2, n_samples)
clf = LinearCSVMC()
dataset = Dataset(X, sa={'targets': y})
selector = SensitivityBasedFeatureSelection(
OneWayAnova(),
FixedNElementTailSelector(3, mode='select', tail='upper'))
selected_dataset = selector(dataset)
clf.train(selected_dataset)
new_sample = np.random.randn(1, n_features)
prediction = clf.predict(new_sample)
