scikit-learn/examples/tutorial/plot_ridge_variance.py

import numpy as np
import pylab as pl

from scikits.learn import linear_model

X_train = np.c_[ .5, 1].T
y_train = [.5, 1]
X_test = np.c_[ 0, 2].T

fig = pl.figure(1, figsize=(4, 3))
pl.clf()
ax = pl.axes([.12, .12, .8, .8])

np.random.seed(0)
ridge = linear_model.Ridge(alpha=.1)

for _ in range(6):
    this_X = .1*np.random.normal(size=(2, 1)) + X_train
    ridge.fit(this_X, y_train)

    ax.plot(X_test, ridge.predict(X_test), color='.5')
    ax.scatter(this_X, y_train, s=3, c='.5', marker='o', zorder=10)

ridge.fit(X_train, y_train)

ax.plot(X_test, ridge.predict(X_test), linewidth=2)
ax.scatter(X_train, y_train, s=30, c='k', marker='+', zorder=10)

ax.set_xticks(())
ax.set_yticks(())
ax.set_xlabel('X')
ax.set_ylabel('y')
ax.set_xlim(0, 2)