ch10-scikit-linear_reg-model_eval.py

import pandas as pd
import matplotlib.pyplot as plt
from sklearn.linear_model import LinearRegression
import numpy as np
from sklearn.cross_validation import train_test_split

df = pd.read_csv('https://archive.ics.uci.edu/ml/machine-learning-databases/housing/housing.data', header=None, sep='\s+')
df.columns = ['CRIM', 'ZN', 'INDUS', 'CHAS',
              'NOX', 'RM', 'AGE', 'DIS', 'RAD',
              'TAX', 'PTRATIO', 'B', 'LSTAT', 'MEDV']


X = df.iloc[:, :-1].values
y = df['MEDV'].values
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=0)
slr = LinearRegression()
slr.fit(X_train, y_train)
y_train_pred = slr.predict(X_train)
y_test_pred = slr.predict(X_test)

plt.scatter(y_train_pred, y_train_pred - y_train, c='blue', marker='o', label='Training data')
plt.scatter(y_test_pred,  y_test_pred - y_test, c='lightgreen', marker='s', label='Test data')
plt.xlabel('Predicted values')
plt.ylabel('Residuals')
plt.legend(loc='upper left')
plt.hlines(y=0, xmin=-10, xmax=50, lw=2, color='red')
plt.xlim([-10, 50])
plt.show()

from sklearn.metrics import mean_squared_error
print('MSE train: %.3f, test: %.3f' % (
        mean_squared_error(y_train, y_train_pred),
        mean_squared_error(y_test, y_test_pred)))

from sklearn.metrics import r2_score
print('R^2 train: %.3f, test: %.3f' %
       (r2_score(y_train, y_train_pred),
       r2_score(y_test, y_test_pred)))



# regularized regression models
from sklearn.linear_model import Ridge
ridge = Ridge(alpha=1.0)

from sklearn.linear_model import Lasso
lasso = Lasso(alpha=1.0)

from sklearn.linear_model import ElasticNet
lasso = ElasticNet(alpha=1.0, l1_ratio=0.5)