from sklearn.datasets import load_boston
from sklearn.model_selection import train_test_split
import time
from sklearn.linear_model import LinearRegression


boston = load_boston()

X = boston.data
y = boston.target

print("X.shape:{}. y.shape:{}".format(X.shape, y.shape))
print('boston.feature_name:{}'.format(boston.feature_names))

X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=3)

model = LinearRegression()

start = time.clock()
model.fit(X_train, y_train)

train_score = model.score(X_train, y_train)
cv_score = model.score(X_test, y_test)

print('time used:{0:.6f}; train_score:{1:.6f}, sv_score:{2:.6f}'.format((time.clock()-start),
                                    train_score, cv_score))

X.shape:(506, 13). y.shape:(506,)
boston.feature_name:['CRIM' 'ZN' 'INDUS' 'CHAS' 'NOX' 'RM' 'AGE' 'DIS' 'RAD' 'TAX' 'PTRATIO'
 'B' 'LSTAT']
time used:0.012403; train_score:0.723941, sv_score:0.794958

from sklearn.datasets import load_boston
from sklearn.model_selection import train_test_split
import time
from sklearn.linear_model import LinearRegression
from sklearn.preprocessing import PolynomialFeatures
from sklearn.pipeline import Pipeline

def polynomial_model(degree=1):
  polynomial_features = PolynomialFeatures(degree=degree, include_bias=False)

  linear_regression = LinearRegression(normalize=True)
  pipeline = Pipeline([('polynomial_features', polynomial_features),
             ('linear_regression', linear_regression)])
  return pipeline

boston = load_boston()
X = boston.data
y = boston.target
print("X.shape:{}. y.shape:{}".format(X.shape, y.shape))
print('boston.feature_name:{}'.format(boston.feature_names))

X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=3)

for i in range(1,4):
  print( 'degree:{}'.format( i ) )
  model = polynomial_model(degree=i)

  start = time.clock()
  model.fit(X_train, y_train)

  train_score = model.score(X_train, y_train)
  cv_score = model.score(X_test, y_test)

  print('time used:{0:.6f}; train_score:{1:.6f}, sv_score:{2:.6f}'.format((time.clock()-start),
                                    train_score, cv_score))

X.shape:(506, 13). y.shape:(506,)
boston.feature_name:['CRIM' 'ZN' 'INDUS' 'CHAS' 'NOX' 'RM' 'AGE' 'DIS' 'RAD' 'TAX' 'PTRATIO'
 'B' 'LSTAT']
degree:1
time used:0.003576; train_score:0.723941, sv_score:0.794958
degree:2
time used:0.030123; train_score:0.930547, sv_score:0.860465
degree:3
time used:0.137346; train_score:1.000000, sv_score:-104.429619