from sklearn.datasets import make_regression
display_bar = '-'*70
X, y = make_regression(
n_samples = 5000,
n_features = 100,
n_informative = 30,
effective_rank = 40,
noise = .1,
)
print display_bar
print "Fit an elastic net on some fake data"
print display_bar
enet = ElasticNet(alpha=.025)
enet.fit(X, y)
print enet
print display_bar
print "Predictions vs. actuals for the last elastic net model:"
print display_bar
preds = enet.predict(X)
print y[:10]
print preds[:10,np.shape(preds)[1]-1]
enet.plot_paths()
from sklearn.datasets import make_regression
display_bar = '-' * 70
X, y = make_regression(
n_samples=5000,
n_features=100,
n_informative=30,
effective_rank=40,
noise=.1,
)
print display_bar
print "Fit an elastic net on some fake data"
print display_bar
enet = ElasticNet(alpha=.025)
enet.fit(X, y)
print enet
print display_bar
print "Predictions vs. actuals for the last elastic net model:"
print display_bar
preds = enet.predict(X)
print y[:10]
print preds[:10, np.shape(preds)[1] - 1]
enet.plot_paths()
